diff --git a/speaker_segmenter/pyannote-v3/SpeakerSegmenter.mlmodelc/LICENSE_NOTICE.txt b/speaker_embedder/pyannote-v3/W16A16/LICENSE_NOTICE.txt similarity index 100% rename from speaker_segmenter/pyannote-v3/SpeakerSegmenter.mlmodelc/LICENSE_NOTICE.txt rename to speaker_embedder/pyannote-v3/W16A16/LICENSE_NOTICE.txt diff --git a/speaker_embedder/pyannote-v3/W16A16/README.txt b/speaker_embedder/pyannote-v3/W16A16/README.txt new file mode 100644 index 0000000000000000000000000000000000000000..38ee63eaf9af8162550ccc0aacba367b59375792 --- /dev/null +++ b/speaker_embedder/pyannote-v3/W16A16/README.txt @@ -0,0 +1,6 @@ +# License + +Original model weights: https://github.com/wenet-e2e/wespeaker/blob/master/docs/pretrained.md#model-license +Argmax-optimized model asset (Assets with `.mlmodelc` extension): https://huggingface.co/argmaxinc/speakerkit-pro/blob/main/LICENSE_NOTICE.txt + +Please contact info@argmaxinc.com for licensing SpeakerKit Pro assets \ No newline at end of file diff --git a/speaker_embedder/pyannote-v3/SpeakerEmbedder.mlmodelc/analytics/coremldata.bin b/speaker_embedder/pyannote-v3/W16A16/SpeakerEmbedder.mlmodelc/analytics/coremldata.bin similarity index 100% rename from speaker_embedder/pyannote-v3/SpeakerEmbedder.mlmodelc/analytics/coremldata.bin rename to speaker_embedder/pyannote-v3/W16A16/SpeakerEmbedder.mlmodelc/analytics/coremldata.bin diff --git a/speaker_embedder/pyannote-v3/SpeakerEmbedder.mlmodelc/coremldata.bin b/speaker_embedder/pyannote-v3/W16A16/SpeakerEmbedder.mlmodelc/coremldata.bin similarity index 100% rename from speaker_embedder/pyannote-v3/SpeakerEmbedder.mlmodelc/coremldata.bin rename to speaker_embedder/pyannote-v3/W16A16/SpeakerEmbedder.mlmodelc/coremldata.bin diff --git a/speaker_embedder/pyannote-v3/SpeakerEmbedder.mlmodelc/metadata.json b/speaker_embedder/pyannote-v3/W16A16/SpeakerEmbedder.mlmodelc/metadata.json similarity index 100% rename from speaker_embedder/pyannote-v3/SpeakerEmbedder.mlmodelc/metadata.json rename to speaker_embedder/pyannote-v3/W16A16/SpeakerEmbedder.mlmodelc/metadata.json diff --git a/speaker_embedder/pyannote-v3/SpeakerEmbedder.mlmodelc/model.mil b/speaker_embedder/pyannote-v3/W16A16/SpeakerEmbedder.mlmodelc/model.mil similarity index 100% rename from speaker_embedder/pyannote-v3/SpeakerEmbedder.mlmodelc/model.mil rename to speaker_embedder/pyannote-v3/W16A16/SpeakerEmbedder.mlmodelc/model.mil diff --git a/speaker_embedder/pyannote-v3/SpeakerEmbedder.mlmodelc/weights/weight.bin b/speaker_embedder/pyannote-v3/W16A16/SpeakerEmbedder.mlmodelc/weights/weight.bin similarity index 100% rename from speaker_embedder/pyannote-v3/SpeakerEmbedder.mlmodelc/weights/weight.bin rename to speaker_embedder/pyannote-v3/W16A16/SpeakerEmbedder.mlmodelc/weights/weight.bin diff --git a/speaker_embedder/pyannote-v3/SpeakerEmbedderPreprocessor.mlmodelc/analytics/coremldata.bin b/speaker_embedder/pyannote-v3/W16A16/SpeakerEmbedderPreprocessor.mlmodelc/analytics/coremldata.bin similarity index 100% rename from speaker_embedder/pyannote-v3/SpeakerEmbedderPreprocessor.mlmodelc/analytics/coremldata.bin rename to speaker_embedder/pyannote-v3/W16A16/SpeakerEmbedderPreprocessor.mlmodelc/analytics/coremldata.bin diff --git a/speaker_embedder/pyannote-v3/SpeakerEmbedderPreprocessor.mlmodelc/coremldata.bin b/speaker_embedder/pyannote-v3/W16A16/SpeakerEmbedderPreprocessor.mlmodelc/coremldata.bin similarity index 100% rename from speaker_embedder/pyannote-v3/SpeakerEmbedderPreprocessor.mlmodelc/coremldata.bin rename to speaker_embedder/pyannote-v3/W16A16/SpeakerEmbedderPreprocessor.mlmodelc/coremldata.bin diff --git a/speaker_embedder/pyannote-v3/SpeakerEmbedderPreprocessor.mlmodelc/metadata.json b/speaker_embedder/pyannote-v3/W16A16/SpeakerEmbedderPreprocessor.mlmodelc/metadata.json similarity index 100% rename from speaker_embedder/pyannote-v3/SpeakerEmbedderPreprocessor.mlmodelc/metadata.json rename to speaker_embedder/pyannote-v3/W16A16/SpeakerEmbedderPreprocessor.mlmodelc/metadata.json diff --git a/speaker_embedder/pyannote-v3/SpeakerEmbedderPreprocessor.mlmodelc/model.mil b/speaker_embedder/pyannote-v3/W16A16/SpeakerEmbedderPreprocessor.mlmodelc/model.mil similarity index 100% rename from speaker_embedder/pyannote-v3/SpeakerEmbedderPreprocessor.mlmodelc/model.mil rename to speaker_embedder/pyannote-v3/W16A16/SpeakerEmbedderPreprocessor.mlmodelc/model.mil diff --git a/speaker_embedder/pyannote-v3/SpeakerEmbedderPreprocessor.mlmodelc/weights/weight.bin b/speaker_embedder/pyannote-v3/W16A16/SpeakerEmbedderPreprocessor.mlmodelc/weights/weight.bin similarity index 100% rename from speaker_embedder/pyannote-v3/SpeakerEmbedderPreprocessor.mlmodelc/weights/weight.bin rename to speaker_embedder/pyannote-v3/W16A16/SpeakerEmbedderPreprocessor.mlmodelc/weights/weight.bin diff --git a/speaker_embedder/pyannote-v3/W6A16/LICENSE_NOTICE.txt b/speaker_embedder/pyannote-v3/W6A16/LICENSE_NOTICE.txt new file mode 100644 index 0000000000000000000000000000000000000000..be2da6c6e6d746ab53f1b21eac16d611aed1193a --- /dev/null +++ b/speaker_embedder/pyannote-v3/W6A16/LICENSE_NOTICE.txt @@ -0,0 +1,7 @@ +Argmax proprietary and confidential. Under NDA. + +Copyright 2024 Argmax, Inc. All rights reserved. + +Unauthorized access, copying, use, distribution, and or commercialization of this file, via any medium or means is strictly prohibited. + +Please contact Argmax for licensing information at info@argmaxinc.com. diff --git a/speaker_embedder/pyannote-v3/W6A16/README.txt b/speaker_embedder/pyannote-v3/W6A16/README.txt new file mode 100644 index 0000000000000000000000000000000000000000..38ee63eaf9af8162550ccc0aacba367b59375792 --- /dev/null +++ b/speaker_embedder/pyannote-v3/W6A16/README.txt @@ -0,0 +1,6 @@ +# License + +Original model weights: https://github.com/wenet-e2e/wespeaker/blob/master/docs/pretrained.md#model-license +Argmax-optimized model asset (Assets with `.mlmodelc` extension): https://huggingface.co/argmaxinc/speakerkit-pro/blob/main/LICENSE_NOTICE.txt + +Please contact info@argmaxinc.com for licensing SpeakerKit Pro assets \ No newline at end of file diff --git a/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedder.mlmodelc/analytics/coremldata.bin b/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedder.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..0661d7a7d4c91cfe8749f0388be5ad06a434f2cc --- /dev/null +++ b/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedder.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e63f6f24c2db3be6678d35a0dc87f34db5d488bd71969cb5fdb816efd6f7f2a5 +size 243 diff --git a/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedder.mlmodelc/coremldata.bin b/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedder.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..b0871551dfc913ef3f5880289571e9e93f9cb888 --- /dev/null +++ b/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedder.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c354879615fe262bc5f8c92b69df8c58111d06331c5d20ddb2e0efe99ea4441c +size 370 diff --git a/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedder.mlmodelc/metadata.json b/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedder.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..1f46bf7fd854b55e00085c4c24262bdbe991ecc5 --- /dev/null +++ b/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedder.mlmodelc/metadata.json @@ -0,0 +1,87 @@ +[ + { + "metadataOutputVersion" : "3.0", + "storagePrecision" : "Mixed (Float16, Palettized (6 bits))", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 1 × 3 × 256)", + "shortDescription" : "", + "shape" : "[1, 3, 256]", + "name" : "speaker_embeddings", + "type" : "MultiArray" + } + ], + "modelParameters" : [ + + ], + "specificationVersion" : 7, + "mlProgramOperationTypeHistogram" : { + "Concat" : 3, + "Ios16.mul" : 12, + "SliceByIndex" : 3, + "Ios16.constexprLutToDense" : 35, + "Transpose" : 1, + "Ios16.sub" : 6, + "Ios16.sqrt" : 3, + "Stack" : 1, + "UpsampleNearestNeighbor" : 1, + "Ios16.conv" : 36, + "Ios16.add" : 22, + "Squeeze" : 1, + "Ios16.relu" : 33, + "Ios16.realDiv" : 9, + "Ios16.reduceSum" : 12, + "ExpandDims" : 8, + "Ios16.linear" : 1, + "Ios16.reshape" : 1 + }, + "computePrecision" : "Mixed (Float16, Float32, Int32)", + "isUpdatable" : "0", + "stateSchema" : [ + + ], + "availability" : { + "macOS" : "13.0", + "tvOS" : "16.0", + "visionOS" : "1.0", + "watchOS" : "9.0", + "iOS" : "16.0", + "macCatalyst" : "16.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "userDefinedMetadata" : { + "com.github.apple.coremltools.source_dialect" : "TorchScript", + "com.github.apple.coremltools.version" : "8.1", + "com.github.apple.coremltools.source" : "torch==2.5.1" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 1 × 998 × 80)", + "shortDescription" : "", + "shape" : "[1, 998, 80]", + "name" : "preprocessor_output_1", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 1 × 3 × 589)", + "shortDescription" : "", + "shape" : "[1, 3, 589]", + "name" : "speaker_masks", + "type" : "MultiArray" + } + ], + "generatedClassName" : "SpeakerEmbedding_6_bit", + "method" : "predict" + } +] \ No newline at end of file diff --git a/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedder.mlmodelc/model.mil b/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..2725f56ac4decef6337454e726c22280971a7e13 --- /dev/null +++ b/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedder.mlmodelc/model.mil @@ -0,0 +1,473 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3401.3.1"}, {"coremlc-version", "3401.4.1"}})] +{ + func main(tensor preprocessor_output_1, tensor speaker_masks) { + tensor var_12 = const()[name = tensor("op_12"), val = tensor(1)]; + tensor var_22 = const()[name = tensor("op_22"), val = tensor([0, 2, 1])]; + tensor input_1_axes_0 = const()[name = tensor("input_1_axes_0"), val = tensor([1])]; + tensor fbank_cast_fp16 = transpose(perm = var_22, x = preprocessor_output_1)[name = tensor("transpose_0")]; + tensor input_1_cast_fp16 = expand_dims(axes = input_1_axes_0, x = fbank_cast_fp16)[name = tensor("input_1_cast_fp16")]; + tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("custom")]; + tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_3_strides_0 = const()[name = tensor("input_3_strides_0"), val = tensor([1, 1])]; + tensor input_3_dilations_0 = const()[name = tensor("input_3_dilations_0"), val = tensor([1, 1])]; + tensor input_3_groups_0 = const()[name = tensor("input_3_groups_0"), val = tensor(1)]; + tensor const_5_to_fp16 = const()[name = tensor("const_5_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor const_6_to_fp16 = const()[name = tensor("const_6_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(704)))]; + tensor input_5_cast_fp16 = conv(bias = const_6_to_fp16, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = const_5_to_fp16, x = input_1_cast_fp16)[name = tensor("input_5_cast_fp16")]; + tensor input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; + tensor input_9_pad_type_0 = const()[name = tensor("input_9_pad_type_0"), val = tensor("custom")]; + tensor input_9_pad_0 = const()[name = tensor("input_9_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_9_strides_0 = const()[name = tensor("input_9_strides_0"), val = tensor([1, 1])]; + tensor input_9_dilations_0 = const()[name = tensor("input_9_dilations_0"), val = tensor([1, 1])]; + tensor input_9_groups_0 = const()[name = tensor("input_9_groups_0"), val = tensor(1)]; + tensor const_7_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7808))), name = tensor("const_7_to_fp16_palettized"), shape = tensor([32, 32, 3, 3])]; + tensor const_8_to_fp16 = const()[name = tensor("const_8_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8000)))]; + tensor input_11_cast_fp16 = conv(bias = const_8_to_fp16, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = const_7_to_fp16_palettized, x = input_7_cast_fp16)[name = tensor("input_11_cast_fp16")]; + tensor input_13_cast_fp16 = relu(x = input_11_cast_fp16)[name = tensor("input_13_cast_fp16")]; + tensor input_15_pad_type_0 = const()[name = tensor("input_15_pad_type_0"), val = tensor("custom")]; + tensor input_15_pad_0 = const()[name = tensor("input_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_15_strides_0 = const()[name = tensor("input_15_strides_0"), val = tensor([1, 1])]; + tensor input_15_dilations_0 = const()[name = tensor("input_15_dilations_0"), val = tensor([1, 1])]; + tensor input_15_groups_0 = const()[name = tensor("input_15_groups_0"), val = tensor(1)]; + tensor const_9_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15104))), name = tensor("const_9_to_fp16_palettized"), shape = tensor([32, 32, 3, 3])]; + tensor const_10_to_fp16 = const()[name = tensor("const_10_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15296)))]; + tensor out_1_cast_fp16 = conv(bias = const_10_to_fp16, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = const_9_to_fp16_palettized, x = input_13_cast_fp16)[name = tensor("out_1_cast_fp16")]; + tensor input_17_cast_fp16 = add(x = out_1_cast_fp16, y = input_7_cast_fp16)[name = tensor("input_17_cast_fp16")]; + tensor input_19_cast_fp16 = relu(x = input_17_cast_fp16)[name = tensor("input_19_cast_fp16")]; + tensor input_21_pad_type_0 = const()[name = tensor("input_21_pad_type_0"), val = tensor("custom")]; + tensor input_21_pad_0 = const()[name = tensor("input_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_21_strides_0 = const()[name = tensor("input_21_strides_0"), val = tensor([1, 1])]; + tensor input_21_dilations_0 = const()[name = tensor("input_21_dilations_0"), val = tensor([1, 1])]; + tensor input_21_groups_0 = const()[name = tensor("input_21_groups_0"), val = tensor(1)]; + tensor const_11_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22400))), name = tensor("const_11_to_fp16_palettized"), shape = tensor([32, 32, 3, 3])]; + tensor const_12_to_fp16 = const()[name = tensor("const_12_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22592)))]; + tensor input_23_cast_fp16 = conv(bias = const_12_to_fp16, dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = const_11_to_fp16_palettized, x = input_19_cast_fp16)[name = tensor("input_23_cast_fp16")]; + tensor input_25_cast_fp16 = relu(x = input_23_cast_fp16)[name = tensor("input_25_cast_fp16")]; + tensor input_27_pad_type_0 = const()[name = tensor("input_27_pad_type_0"), val = tensor("custom")]; + tensor input_27_pad_0 = const()[name = tensor("input_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_27_strides_0 = const()[name = tensor("input_27_strides_0"), val = tensor([1, 1])]; + tensor input_27_dilations_0 = const()[name = tensor("input_27_dilations_0"), val = tensor([1, 1])]; + tensor input_27_groups_0 = const()[name = tensor("input_27_groups_0"), val = tensor(1)]; + tensor const_13_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29696))), name = tensor("const_13_to_fp16_palettized"), shape = tensor([32, 32, 3, 3])]; + tensor const_14_to_fp16 = const()[name = tensor("const_14_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29888)))]; + tensor out_3_cast_fp16 = conv(bias = const_14_to_fp16, dilations = input_27_dilations_0, groups = input_27_groups_0, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = input_27_strides_0, weight = const_13_to_fp16_palettized, x = input_25_cast_fp16)[name = tensor("out_3_cast_fp16")]; + tensor input_29_cast_fp16 = add(x = out_3_cast_fp16, y = input_19_cast_fp16)[name = tensor("input_29_cast_fp16")]; + tensor input_31_cast_fp16 = relu(x = input_29_cast_fp16)[name = tensor("input_31_cast_fp16")]; + tensor input_33_pad_type_0 = const()[name = tensor("input_33_pad_type_0"), val = tensor("custom")]; + tensor input_33_pad_0 = const()[name = tensor("input_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_33_strides_0 = const()[name = tensor("input_33_strides_0"), val = tensor([1, 1])]; + tensor input_33_dilations_0 = const()[name = tensor("input_33_dilations_0"), val = tensor([1, 1])]; + tensor input_33_groups_0 = const()[name = tensor("input_33_groups_0"), val = tensor(1)]; + tensor const_15_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(36992))), name = tensor("const_15_to_fp16_palettized"), shape = tensor([32, 32, 3, 3])]; + tensor const_16_to_fp16 = const()[name = tensor("const_16_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37184)))]; + tensor input_35_cast_fp16 = conv(bias = const_16_to_fp16, dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = const_15_to_fp16_palettized, x = input_31_cast_fp16)[name = tensor("input_35_cast_fp16")]; + tensor input_37_cast_fp16 = relu(x = input_35_cast_fp16)[name = tensor("input_37_cast_fp16")]; + tensor input_39_pad_type_0 = const()[name = tensor("input_39_pad_type_0"), val = tensor("custom")]; + tensor input_39_pad_0 = const()[name = tensor("input_39_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_39_strides_0 = const()[name = tensor("input_39_strides_0"), val = tensor([1, 1])]; + tensor input_39_dilations_0 = const()[name = tensor("input_39_dilations_0"), val = tensor([1, 1])]; + tensor input_39_groups_0 = const()[name = tensor("input_39_groups_0"), val = tensor(1)]; + tensor const_17_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37312))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44288))), name = tensor("const_17_to_fp16_palettized"), shape = tensor([32, 32, 3, 3])]; + tensor const_18_to_fp16 = const()[name = tensor("const_18_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44480)))]; + tensor out_5_cast_fp16 = conv(bias = const_18_to_fp16, dilations = input_39_dilations_0, groups = input_39_groups_0, pad = input_39_pad_0, pad_type = input_39_pad_type_0, strides = input_39_strides_0, weight = const_17_to_fp16_palettized, x = input_37_cast_fp16)[name = tensor("out_5_cast_fp16")]; + tensor input_41_cast_fp16 = add(x = out_5_cast_fp16, y = input_31_cast_fp16)[name = tensor("input_41_cast_fp16")]; + tensor input_43_cast_fp16 = relu(x = input_41_cast_fp16)[name = tensor("input_43_cast_fp16")]; + tensor input_45_pad_type_0 = const()[name = tensor("input_45_pad_type_0"), val = tensor("custom")]; + tensor input_45_pad_0 = const()[name = tensor("input_45_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_45_strides_0 = const()[name = tensor("input_45_strides_0"), val = tensor([2, 2])]; + tensor input_45_dilations_0 = const()[name = tensor("input_45_dilations_0"), val = tensor([1, 1])]; + tensor input_45_groups_0 = const()[name = tensor("input_45_groups_0"), val = tensor(1)]; + tensor const_19_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58496))), name = tensor("const_19_to_fp16_palettized"), shape = tensor([64, 32, 3, 3])]; + tensor const_20_to_fp16 = const()[name = tensor("const_20_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58688)))]; + tensor input_47_cast_fp16 = conv(bias = const_20_to_fp16, dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = const_19_to_fp16_palettized, x = input_43_cast_fp16)[name = tensor("input_47_cast_fp16")]; + tensor input_49_cast_fp16 = relu(x = input_47_cast_fp16)[name = tensor("input_49_cast_fp16")]; + tensor input_51_pad_type_0 = const()[name = tensor("input_51_pad_type_0"), val = tensor("custom")]; + tensor input_51_pad_0 = const()[name = tensor("input_51_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_51_strides_0 = const()[name = tensor("input_51_strides_0"), val = tensor([1, 1])]; + tensor input_51_dilations_0 = const()[name = tensor("input_51_dilations_0"), val = tensor([1, 1])]; + tensor input_51_groups_0 = const()[name = tensor("input_51_groups_0"), val = tensor(1)]; + tensor const_21_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86592))), name = tensor("const_21_to_fp16_palettized"), shape = tensor([64, 64, 3, 3])]; + tensor const_22_to_fp16 = const()[name = tensor("const_22_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86784)))]; + tensor out_7_cast_fp16 = conv(bias = const_22_to_fp16, dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = const_21_to_fp16_palettized, x = input_49_cast_fp16)[name = tensor("out_7_cast_fp16")]; + tensor input_53_pad_type_0 = const()[name = tensor("input_53_pad_type_0"), val = tensor("valid")]; + tensor input_53_strides_0 = const()[name = tensor("input_53_strides_0"), val = tensor([2, 2])]; + tensor input_53_pad_0 = const()[name = tensor("input_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_53_dilations_0 = const()[name = tensor("input_53_dilations_0"), val = tensor([1, 1])]; + tensor input_53_groups_0 = const()[name = tensor("input_53_groups_0"), val = tensor(1)]; + tensor const_23_to_fp16 = const()[name = tensor("const_23_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(86976)))]; + tensor const_24_to_fp16 = const()[name = tensor("const_24_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91136)))]; + tensor var_171_cast_fp16 = conv(bias = const_24_to_fp16, dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = const_23_to_fp16, x = input_43_cast_fp16)[name = tensor("op_171_cast_fp16")]; + tensor input_55_cast_fp16 = add(x = out_7_cast_fp16, y = var_171_cast_fp16)[name = tensor("input_55_cast_fp16")]; + tensor input_57_cast_fp16 = relu(x = input_55_cast_fp16)[name = tensor("input_57_cast_fp16")]; + tensor input_59_pad_type_0 = const()[name = tensor("input_59_pad_type_0"), val = tensor("custom")]; + tensor input_59_pad_0 = const()[name = tensor("input_59_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_59_strides_0 = const()[name = tensor("input_59_strides_0"), val = tensor([1, 1])]; + tensor input_59_dilations_0 = const()[name = tensor("input_59_dilations_0"), val = tensor([1, 1])]; + tensor input_59_groups_0 = const()[name = tensor("input_59_groups_0"), val = tensor(1)]; + tensor const_25_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(91328))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119040))), name = tensor("const_25_to_fp16_palettized"), shape = tensor([64, 64, 3, 3])]; + tensor const_26_to_fp16 = const()[name = tensor("const_26_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119232)))]; + tensor input_61_cast_fp16 = conv(bias = const_26_to_fp16, dilations = input_59_dilations_0, groups = input_59_groups_0, pad = input_59_pad_0, pad_type = input_59_pad_type_0, strides = input_59_strides_0, weight = const_25_to_fp16_palettized, x = input_57_cast_fp16)[name = tensor("input_61_cast_fp16")]; + tensor input_63_cast_fp16 = relu(x = input_61_cast_fp16)[name = tensor("input_63_cast_fp16")]; + tensor input_65_pad_type_0 = const()[name = tensor("input_65_pad_type_0"), val = tensor("custom")]; + tensor input_65_pad_0 = const()[name = tensor("input_65_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_65_strides_0 = const()[name = tensor("input_65_strides_0"), val = tensor([1, 1])]; + tensor input_65_dilations_0 = const()[name = tensor("input_65_dilations_0"), val = tensor([1, 1])]; + tensor input_65_groups_0 = const()[name = tensor("input_65_groups_0"), val = tensor(1)]; + tensor const_27_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147136))), name = tensor("const_27_to_fp16_palettized"), shape = tensor([64, 64, 3, 3])]; + tensor const_28_to_fp16 = const()[name = tensor("const_28_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147328)))]; + tensor out_9_cast_fp16 = conv(bias = const_28_to_fp16, dilations = input_65_dilations_0, groups = input_65_groups_0, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = input_65_strides_0, weight = const_27_to_fp16_palettized, x = input_63_cast_fp16)[name = tensor("out_9_cast_fp16")]; + tensor input_67_cast_fp16 = add(x = out_9_cast_fp16, y = input_57_cast_fp16)[name = tensor("input_67_cast_fp16")]; + tensor input_69_cast_fp16 = relu(x = input_67_cast_fp16)[name = tensor("input_69_cast_fp16")]; + tensor input_71_pad_type_0 = const()[name = tensor("input_71_pad_type_0"), val = tensor("custom")]; + tensor input_71_pad_0 = const()[name = tensor("input_71_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_71_strides_0 = const()[name = tensor("input_71_strides_0"), val = tensor([1, 1])]; + tensor input_71_dilations_0 = const()[name = tensor("input_71_dilations_0"), val = tensor([1, 1])]; + tensor input_71_groups_0 = const()[name = tensor("input_71_groups_0"), val = tensor(1)]; + tensor const_29_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175232))), name = tensor("const_29_to_fp16_palettized"), shape = tensor([64, 64, 3, 3])]; + tensor const_30_to_fp16 = const()[name = tensor("const_30_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175424)))]; + tensor input_73_cast_fp16 = conv(bias = const_30_to_fp16, dilations = input_71_dilations_0, groups = input_71_groups_0, pad = input_71_pad_0, pad_type = input_71_pad_type_0, strides = input_71_strides_0, weight = const_29_to_fp16_palettized, x = input_69_cast_fp16)[name = tensor("input_73_cast_fp16")]; + tensor input_75_cast_fp16 = relu(x = input_73_cast_fp16)[name = tensor("input_75_cast_fp16")]; + tensor input_77_pad_type_0 = const()[name = tensor("input_77_pad_type_0"), val = tensor("custom")]; + tensor input_77_pad_0 = const()[name = tensor("input_77_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_77_strides_0 = const()[name = tensor("input_77_strides_0"), val = tensor([1, 1])]; + tensor input_77_dilations_0 = const()[name = tensor("input_77_dilations_0"), val = tensor([1, 1])]; + tensor input_77_groups_0 = const()[name = tensor("input_77_groups_0"), val = tensor(1)]; + tensor const_31_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(203328))), name = tensor("const_31_to_fp16_palettized"), shape = tensor([64, 64, 3, 3])]; + tensor const_32_to_fp16 = const()[name = tensor("const_32_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(203520)))]; + tensor out_11_cast_fp16 = conv(bias = const_32_to_fp16, dilations = input_77_dilations_0, groups = input_77_groups_0, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = input_77_strides_0, weight = const_31_to_fp16_palettized, x = input_75_cast_fp16)[name = tensor("out_11_cast_fp16")]; + tensor input_79_cast_fp16 = add(x = out_11_cast_fp16, y = input_69_cast_fp16)[name = tensor("input_79_cast_fp16")]; + tensor input_81_cast_fp16 = relu(x = input_79_cast_fp16)[name = tensor("input_81_cast_fp16")]; + tensor input_83_pad_type_0 = const()[name = tensor("input_83_pad_type_0"), val = tensor("custom")]; + tensor input_83_pad_0 = const()[name = tensor("input_83_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_83_strides_0 = const()[name = tensor("input_83_strides_0"), val = tensor([1, 1])]; + tensor input_83_dilations_0 = const()[name = tensor("input_83_dilations_0"), val = tensor([1, 1])]; + tensor input_83_groups_0 = const()[name = tensor("input_83_groups_0"), val = tensor(1)]; + tensor const_33_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(203712))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(231424))), name = tensor("const_33_to_fp16_palettized"), shape = tensor([64, 64, 3, 3])]; + tensor const_34_to_fp16 = const()[name = tensor("const_34_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(231616)))]; + tensor input_85_cast_fp16 = conv(bias = const_34_to_fp16, dilations = input_83_dilations_0, groups = input_83_groups_0, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = input_83_strides_0, weight = const_33_to_fp16_palettized, x = input_81_cast_fp16)[name = tensor("input_85_cast_fp16")]; + tensor input_87_cast_fp16 = relu(x = input_85_cast_fp16)[name = tensor("input_87_cast_fp16")]; + tensor input_89_pad_type_0 = const()[name = tensor("input_89_pad_type_0"), val = tensor("custom")]; + tensor input_89_pad_0 = const()[name = tensor("input_89_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_89_strides_0 = const()[name = tensor("input_89_strides_0"), val = tensor([1, 1])]; + tensor input_89_dilations_0 = const()[name = tensor("input_89_dilations_0"), val = tensor([1, 1])]; + tensor input_89_groups_0 = const()[name = tensor("input_89_groups_0"), val = tensor(1)]; + tensor const_35_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(231808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(259520))), name = tensor("const_35_to_fp16_palettized"), shape = tensor([64, 64, 3, 3])]; + tensor const_36_to_fp16 = const()[name = tensor("const_36_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(259712)))]; + tensor out_13_cast_fp16 = conv(bias = const_36_to_fp16, dilations = input_89_dilations_0, groups = input_89_groups_0, pad = input_89_pad_0, pad_type = input_89_pad_type_0, strides = input_89_strides_0, weight = const_35_to_fp16_palettized, x = input_87_cast_fp16)[name = tensor("out_13_cast_fp16")]; + tensor input_91_cast_fp16 = add(x = out_13_cast_fp16, y = input_81_cast_fp16)[name = tensor("input_91_cast_fp16")]; + tensor input_93_cast_fp16 = relu(x = input_91_cast_fp16)[name = tensor("input_93_cast_fp16")]; + tensor input_95_pad_type_0 = const()[name = tensor("input_95_pad_type_0"), val = tensor("custom")]; + tensor input_95_pad_0 = const()[name = tensor("input_95_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_95_strides_0 = const()[name = tensor("input_95_strides_0"), val = tensor([2, 2])]; + tensor input_95_dilations_0 = const()[name = tensor("input_95_dilations_0"), val = tensor([1, 1])]; + tensor input_95_groups_0 = const()[name = tensor("input_95_groups_0"), val = tensor(1)]; + tensor const_37_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(259904))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(315264))), name = tensor("const_37_to_fp16_palettized"), shape = tensor([128, 64, 3, 3])]; + tensor const_38_to_fp16 = const()[name = tensor("const_38_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(315456)))]; + tensor input_97_cast_fp16 = conv(bias = const_38_to_fp16, dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = const_37_to_fp16_palettized, x = input_93_cast_fp16)[name = tensor("input_97_cast_fp16")]; + tensor input_99_cast_fp16 = relu(x = input_97_cast_fp16)[name = tensor("input_99_cast_fp16")]; + tensor input_101_pad_type_0 = const()[name = tensor("input_101_pad_type_0"), val = tensor("custom")]; + tensor input_101_pad_0 = const()[name = tensor("input_101_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_101_strides_0 = const()[name = tensor("input_101_strides_0"), val = tensor([1, 1])]; + tensor input_101_dilations_0 = const()[name = tensor("input_101_dilations_0"), val = tensor([1, 1])]; + tensor input_101_groups_0 = const()[name = tensor("input_101_groups_0"), val = tensor(1)]; + tensor const_39_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(315776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426432))), name = tensor("const_39_to_fp16_palettized"), shape = tensor([128, 128, 3, 3])]; + tensor const_40_to_fp16 = const()[name = tensor("const_40_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426624)))]; + tensor out_15_cast_fp16 = conv(bias = const_40_to_fp16, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_39_to_fp16_palettized, x = input_99_cast_fp16)[name = tensor("out_15_cast_fp16")]; + tensor input_103_pad_type_0 = const()[name = tensor("input_103_pad_type_0"), val = tensor("valid")]; + tensor input_103_strides_0 = const()[name = tensor("input_103_strides_0"), val = tensor([2, 2])]; + tensor input_103_pad_0 = const()[name = tensor("input_103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_103_dilations_0 = const()[name = tensor("input_103_dilations_0"), val = tensor([1, 1])]; + tensor input_103_groups_0 = const()[name = tensor("input_103_groups_0"), val = tensor(1)]; + tensor const_41_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426944))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(433152))), name = tensor("const_41_to_fp16_palettized"), shape = tensor([128, 64, 1, 1])]; + tensor const_42_to_fp16 = const()[name = tensor("const_42_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(433344)))]; + tensor var_307_cast_fp16 = conv(bias = const_42_to_fp16, dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = const_41_to_fp16_palettized, x = input_93_cast_fp16)[name = tensor("op_307_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = out_15_cast_fp16, y = var_307_cast_fp16)[name = tensor("input_105_cast_fp16")]; + tensor input_107_cast_fp16 = relu(x = input_105_cast_fp16)[name = tensor("input_107_cast_fp16")]; + tensor input_109_pad_type_0 = const()[name = tensor("input_109_pad_type_0"), val = tensor("custom")]; + tensor input_109_pad_0 = const()[name = tensor("input_109_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_109_strides_0 = const()[name = tensor("input_109_strides_0"), val = tensor([1, 1])]; + tensor input_109_dilations_0 = const()[name = tensor("input_109_dilations_0"), val = tensor([1, 1])]; + tensor input_109_groups_0 = const()[name = tensor("input_109_groups_0"), val = tensor(1)]; + tensor const_43_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(433664))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(544320))), name = tensor("const_43_to_fp16_palettized"), shape = tensor([128, 128, 3, 3])]; + tensor const_44_to_fp16 = const()[name = tensor("const_44_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(544512)))]; + tensor input_111_cast_fp16 = conv(bias = const_44_to_fp16, dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = const_43_to_fp16_palettized, x = input_107_cast_fp16)[name = tensor("input_111_cast_fp16")]; + tensor input_113_cast_fp16 = relu(x = input_111_cast_fp16)[name = tensor("input_113_cast_fp16")]; + tensor input_115_pad_type_0 = const()[name = tensor("input_115_pad_type_0"), val = tensor("custom")]; + tensor input_115_pad_0 = const()[name = tensor("input_115_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_115_strides_0 = const()[name = tensor("input_115_strides_0"), val = tensor([1, 1])]; + tensor input_115_dilations_0 = const()[name = tensor("input_115_dilations_0"), val = tensor([1, 1])]; + tensor input_115_groups_0 = const()[name = tensor("input_115_groups_0"), val = tensor(1)]; + tensor const_45_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(544832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655488))), name = tensor("const_45_to_fp16_palettized"), shape = tensor([128, 128, 3, 3])]; + tensor const_46_to_fp16 = const()[name = tensor("const_46_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655680)))]; + tensor out_17_cast_fp16 = conv(bias = const_46_to_fp16, dilations = input_115_dilations_0, groups = input_115_groups_0, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = input_115_strides_0, weight = const_45_to_fp16_palettized, x = input_113_cast_fp16)[name = tensor("out_17_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = out_17_cast_fp16, y = input_107_cast_fp16)[name = tensor("input_117_cast_fp16")]; + tensor input_119_cast_fp16 = relu(x = input_117_cast_fp16)[name = tensor("input_119_cast_fp16")]; + tensor input_121_pad_type_0 = const()[name = tensor("input_121_pad_type_0"), val = tensor("custom")]; + tensor input_121_pad_0 = const()[name = tensor("input_121_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_121_strides_0 = const()[name = tensor("input_121_strides_0"), val = tensor([1, 1])]; + tensor input_121_dilations_0 = const()[name = tensor("input_121_dilations_0"), val = tensor([1, 1])]; + tensor input_121_groups_0 = const()[name = tensor("input_121_groups_0"), val = tensor(1)]; + tensor const_47_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(656000))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(766656))), name = tensor("const_47_to_fp16_palettized"), shape = tensor([128, 128, 3, 3])]; + tensor const_48_to_fp16 = const()[name = tensor("const_48_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(766848)))]; + tensor input_123_cast_fp16 = conv(bias = const_48_to_fp16, dilations = input_121_dilations_0, groups = input_121_groups_0, pad = input_121_pad_0, pad_type = input_121_pad_type_0, strides = input_121_strides_0, weight = const_47_to_fp16_palettized, x = input_119_cast_fp16)[name = tensor("input_123_cast_fp16")]; + tensor input_125_cast_fp16 = relu(x = input_123_cast_fp16)[name = tensor("input_125_cast_fp16")]; + tensor input_127_pad_type_0 = const()[name = tensor("input_127_pad_type_0"), val = tensor("custom")]; + tensor input_127_pad_0 = const()[name = tensor("input_127_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_127_strides_0 = const()[name = tensor("input_127_strides_0"), val = tensor([1, 1])]; + tensor input_127_dilations_0 = const()[name = tensor("input_127_dilations_0"), val = tensor([1, 1])]; + tensor input_127_groups_0 = const()[name = tensor("input_127_groups_0"), val = tensor(1)]; + tensor const_49_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(767168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(877824))), name = tensor("const_49_to_fp16_palettized"), shape = tensor([128, 128, 3, 3])]; + tensor const_50_to_fp16 = const()[name = tensor("const_50_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(878016)))]; + tensor out_19_cast_fp16 = conv(bias = const_50_to_fp16, dilations = input_127_dilations_0, groups = input_127_groups_0, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = input_127_strides_0, weight = const_49_to_fp16_palettized, x = input_125_cast_fp16)[name = tensor("out_19_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = out_19_cast_fp16, y = input_119_cast_fp16)[name = tensor("input_129_cast_fp16")]; + tensor input_131_cast_fp16 = relu(x = input_129_cast_fp16)[name = tensor("input_131_cast_fp16")]; + tensor input_133_pad_type_0 = const()[name = tensor("input_133_pad_type_0"), val = tensor("custom")]; + tensor input_133_pad_0 = const()[name = tensor("input_133_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_133_strides_0 = const()[name = tensor("input_133_strides_0"), val = tensor([1, 1])]; + tensor input_133_dilations_0 = const()[name = tensor("input_133_dilations_0"), val = tensor([1, 1])]; + tensor input_133_groups_0 = const()[name = tensor("input_133_groups_0"), val = tensor(1)]; + tensor const_51_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(878336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(988992))), name = tensor("const_51_to_fp16_palettized"), shape = tensor([128, 128, 3, 3])]; + tensor const_52_to_fp16 = const()[name = tensor("const_52_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(989184)))]; + tensor input_135_cast_fp16 = conv(bias = const_52_to_fp16, dilations = input_133_dilations_0, groups = input_133_groups_0, pad = input_133_pad_0, pad_type = input_133_pad_type_0, strides = input_133_strides_0, weight = const_51_to_fp16_palettized, x = input_131_cast_fp16)[name = tensor("input_135_cast_fp16")]; + tensor input_137_cast_fp16 = relu(x = input_135_cast_fp16)[name = tensor("input_137_cast_fp16")]; + tensor input_139_pad_type_0 = const()[name = tensor("input_139_pad_type_0"), val = tensor("custom")]; + tensor input_139_pad_0 = const()[name = tensor("input_139_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_139_strides_0 = const()[name = tensor("input_139_strides_0"), val = tensor([1, 1])]; + tensor input_139_dilations_0 = const()[name = tensor("input_139_dilations_0"), val = tensor([1, 1])]; + tensor input_139_groups_0 = const()[name = tensor("input_139_groups_0"), val = tensor(1)]; + tensor const_53_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(989504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1100160))), name = tensor("const_53_to_fp16_palettized"), shape = tensor([128, 128, 3, 3])]; + tensor const_54_to_fp16 = const()[name = tensor("const_54_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1100352)))]; + tensor out_21_cast_fp16 = conv(bias = const_54_to_fp16, dilations = input_139_dilations_0, groups = input_139_groups_0, pad = input_139_pad_0, pad_type = input_139_pad_type_0, strides = input_139_strides_0, weight = const_53_to_fp16_palettized, x = input_137_cast_fp16)[name = tensor("out_21_cast_fp16")]; + tensor input_141_cast_fp16 = add(x = out_21_cast_fp16, y = input_131_cast_fp16)[name = tensor("input_141_cast_fp16")]; + tensor input_143_cast_fp16 = relu(x = input_141_cast_fp16)[name = tensor("input_143_cast_fp16")]; + tensor input_145_pad_type_0 = const()[name = tensor("input_145_pad_type_0"), val = tensor("custom")]; + tensor input_145_pad_0 = const()[name = tensor("input_145_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_145_strides_0 = const()[name = tensor("input_145_strides_0"), val = tensor([1, 1])]; + tensor input_145_dilations_0 = const()[name = tensor("input_145_dilations_0"), val = tensor([1, 1])]; + tensor input_145_groups_0 = const()[name = tensor("input_145_groups_0"), val = tensor(1)]; + tensor const_55_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1100672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1211328))), name = tensor("const_55_to_fp16_palettized"), shape = tensor([128, 128, 3, 3])]; + tensor const_56_to_fp16 = const()[name = tensor("const_56_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1211520)))]; + tensor input_147_cast_fp16 = conv(bias = const_56_to_fp16, dilations = input_145_dilations_0, groups = input_145_groups_0, pad = input_145_pad_0, pad_type = input_145_pad_type_0, strides = input_145_strides_0, weight = const_55_to_fp16_palettized, x = input_143_cast_fp16)[name = tensor("input_147_cast_fp16")]; + tensor input_149_cast_fp16 = relu(x = input_147_cast_fp16)[name = tensor("input_149_cast_fp16")]; + tensor input_151_pad_type_0 = const()[name = tensor("input_151_pad_type_0"), val = tensor("custom")]; + tensor input_151_pad_0 = const()[name = tensor("input_151_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_151_strides_0 = const()[name = tensor("input_151_strides_0"), val = tensor([1, 1])]; + tensor input_151_dilations_0 = const()[name = tensor("input_151_dilations_0"), val = tensor([1, 1])]; + tensor input_151_groups_0 = const()[name = tensor("input_151_groups_0"), val = tensor(1)]; + tensor const_57_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1211840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1322496))), name = tensor("const_57_to_fp16_palettized"), shape = tensor([128, 128, 3, 3])]; + tensor const_58_to_fp16 = const()[name = tensor("const_58_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1322688)))]; + tensor out_23_cast_fp16 = conv(bias = const_58_to_fp16, dilations = input_151_dilations_0, groups = input_151_groups_0, pad = input_151_pad_0, pad_type = input_151_pad_type_0, strides = input_151_strides_0, weight = const_57_to_fp16_palettized, x = input_149_cast_fp16)[name = tensor("out_23_cast_fp16")]; + tensor input_153_cast_fp16 = add(x = out_23_cast_fp16, y = input_143_cast_fp16)[name = tensor("input_153_cast_fp16")]; + tensor input_155_cast_fp16 = relu(x = input_153_cast_fp16)[name = tensor("input_155_cast_fp16")]; + tensor input_157_pad_type_0 = const()[name = tensor("input_157_pad_type_0"), val = tensor("custom")]; + tensor input_157_pad_0 = const()[name = tensor("input_157_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_157_strides_0 = const()[name = tensor("input_157_strides_0"), val = tensor([1, 1])]; + tensor input_157_dilations_0 = const()[name = tensor("input_157_dilations_0"), val = tensor([1, 1])]; + tensor input_157_groups_0 = const()[name = tensor("input_157_groups_0"), val = tensor(1)]; + tensor const_59_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1323008))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1433664))), name = tensor("const_59_to_fp16_palettized"), shape = tensor([128, 128, 3, 3])]; + tensor const_60_to_fp16 = const()[name = tensor("const_60_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1433856)))]; + tensor input_159_cast_fp16 = conv(bias = const_60_to_fp16, dilations = input_157_dilations_0, groups = input_157_groups_0, pad = input_157_pad_0, pad_type = input_157_pad_type_0, strides = input_157_strides_0, weight = const_59_to_fp16_palettized, x = input_155_cast_fp16)[name = tensor("input_159_cast_fp16")]; + tensor input_161_cast_fp16 = relu(x = input_159_cast_fp16)[name = tensor("input_161_cast_fp16")]; + tensor input_163_pad_type_0 = const()[name = tensor("input_163_pad_type_0"), val = tensor("custom")]; + tensor input_163_pad_0 = const()[name = tensor("input_163_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_163_strides_0 = const()[name = tensor("input_163_strides_0"), val = tensor([1, 1])]; + tensor input_163_dilations_0 = const()[name = tensor("input_163_dilations_0"), val = tensor([1, 1])]; + tensor input_163_groups_0 = const()[name = tensor("input_163_groups_0"), val = tensor(1)]; + tensor const_61_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1434176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1544832))), name = tensor("const_61_to_fp16_palettized"), shape = tensor([128, 128, 3, 3])]; + tensor const_62_to_fp16 = const()[name = tensor("const_62_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1545024)))]; + tensor out_25_cast_fp16 = conv(bias = const_62_to_fp16, dilations = input_163_dilations_0, groups = input_163_groups_0, pad = input_163_pad_0, pad_type = input_163_pad_type_0, strides = input_163_strides_0, weight = const_61_to_fp16_palettized, x = input_161_cast_fp16)[name = tensor("out_25_cast_fp16")]; + tensor input_165_cast_fp16 = add(x = out_25_cast_fp16, y = input_155_cast_fp16)[name = tensor("input_165_cast_fp16")]; + tensor input_167_cast_fp16 = relu(x = input_165_cast_fp16)[name = tensor("input_167_cast_fp16")]; + tensor input_169_pad_type_0 = const()[name = tensor("input_169_pad_type_0"), val = tensor("custom")]; + tensor input_169_pad_0 = const()[name = tensor("input_169_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_169_strides_0 = const()[name = tensor("input_169_strides_0"), val = tensor([2, 2])]; + tensor input_169_dilations_0 = const()[name = tensor("input_169_dilations_0"), val = tensor([1, 1])]; + tensor input_169_groups_0 = const()[name = tensor("input_169_groups_0"), val = tensor(1)]; + tensor const_63_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1545344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1766592))), name = tensor("const_63_to_fp16_palettized"), shape = tensor([256, 128, 3, 3])]; + tensor const_64_to_fp16 = const()[name = tensor("const_64_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1766784)))]; + tensor input_171_cast_fp16 = conv(bias = const_64_to_fp16, dilations = input_169_dilations_0, groups = input_169_groups_0, pad = input_169_pad_0, pad_type = input_169_pad_type_0, strides = input_169_strides_0, weight = const_63_to_fp16_palettized, x = input_167_cast_fp16)[name = tensor("input_171_cast_fp16")]; + tensor input_173_cast_fp16 = relu(x = input_171_cast_fp16)[name = tensor("input_173_cast_fp16")]; + tensor input_175_pad_type_0 = const()[name = tensor("input_175_pad_type_0"), val = tensor("custom")]; + tensor input_175_pad_0 = const()[name = tensor("input_175_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_175_strides_0 = const()[name = tensor("input_175_strides_0"), val = tensor([1, 1])]; + tensor input_175_dilations_0 = const()[name = tensor("input_175_dilations_0"), val = tensor([1, 1])]; + tensor input_175_groups_0 = const()[name = tensor("input_175_groups_0"), val = tensor(1)]; + tensor const_65_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1767360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2209792))), name = tensor("const_65_to_fp16_palettized"), shape = tensor([256, 256, 3, 3])]; + tensor const_66_to_fp16 = const()[name = tensor("const_66_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2209984)))]; + tensor out_27_cast_fp16 = conv(bias = const_66_to_fp16, dilations = input_175_dilations_0, groups = input_175_groups_0, pad = input_175_pad_0, pad_type = input_175_pad_type_0, strides = input_175_strides_0, weight = const_65_to_fp16_palettized, x = input_173_cast_fp16)[name = tensor("out_27_cast_fp16")]; + tensor input_177_pad_type_0 = const()[name = tensor("input_177_pad_type_0"), val = tensor("valid")]; + tensor input_177_strides_0 = const()[name = tensor("input_177_strides_0"), val = tensor([2, 2])]; + tensor input_177_pad_0 = const()[name = tensor("input_177_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_177_dilations_0 = const()[name = tensor("input_177_dilations_0"), val = tensor([1, 1])]; + tensor input_177_groups_0 = const()[name = tensor("input_177_groups_0"), val = tensor(1)]; + tensor const_67_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2210560))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2235200))), name = tensor("const_67_to_fp16_palettized"), shape = tensor([256, 128, 1, 1])]; + tensor const_68_to_fp16 = const()[name = tensor("const_68_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2235392)))]; + tensor var_498_cast_fp16 = conv(bias = const_68_to_fp16, dilations = input_177_dilations_0, groups = input_177_groups_0, pad = input_177_pad_0, pad_type = input_177_pad_type_0, strides = input_177_strides_0, weight = const_67_to_fp16_palettized, x = input_167_cast_fp16)[name = tensor("op_498_cast_fp16")]; + tensor input_179_cast_fp16 = add(x = out_27_cast_fp16, y = var_498_cast_fp16)[name = tensor("input_179_cast_fp16")]; + tensor input_181_cast_fp16 = relu(x = input_179_cast_fp16)[name = tensor("input_181_cast_fp16")]; + tensor input_183_pad_type_0 = const()[name = tensor("input_183_pad_type_0"), val = tensor("custom")]; + tensor input_183_pad_0 = const()[name = tensor("input_183_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_183_strides_0 = const()[name = tensor("input_183_strides_0"), val = tensor([1, 1])]; + tensor input_183_dilations_0 = const()[name = tensor("input_183_dilations_0"), val = tensor([1, 1])]; + tensor input_183_groups_0 = const()[name = tensor("input_183_groups_0"), val = tensor(1)]; + tensor const_69_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2235968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2678400))), name = tensor("const_69_to_fp16_palettized"), shape = tensor([256, 256, 3, 3])]; + tensor const_70_to_fp16 = const()[name = tensor("const_70_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2678592)))]; + tensor input_185_cast_fp16 = conv(bias = const_70_to_fp16, dilations = input_183_dilations_0, groups = input_183_groups_0, pad = input_183_pad_0, pad_type = input_183_pad_type_0, strides = input_183_strides_0, weight = const_69_to_fp16_palettized, x = input_181_cast_fp16)[name = tensor("input_185_cast_fp16")]; + tensor input_187_cast_fp16 = relu(x = input_185_cast_fp16)[name = tensor("input_187_cast_fp16")]; + tensor input_189_pad_type_0 = const()[name = tensor("input_189_pad_type_0"), val = tensor("custom")]; + tensor input_189_pad_0 = const()[name = tensor("input_189_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_189_strides_0 = const()[name = tensor("input_189_strides_0"), val = tensor([1, 1])]; + tensor input_189_dilations_0 = const()[name = tensor("input_189_dilations_0"), val = tensor([1, 1])]; + tensor input_189_groups_0 = const()[name = tensor("input_189_groups_0"), val = tensor(1)]; + tensor const_71_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2679168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3121600))), name = tensor("const_71_to_fp16_palettized"), shape = tensor([256, 256, 3, 3])]; + tensor const_72_to_fp16 = const()[name = tensor("const_72_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3121792)))]; + tensor out_29_cast_fp16 = conv(bias = const_72_to_fp16, dilations = input_189_dilations_0, groups = input_189_groups_0, pad = input_189_pad_0, pad_type = input_189_pad_type_0, strides = input_189_strides_0, weight = const_71_to_fp16_palettized, x = input_187_cast_fp16)[name = tensor("out_29_cast_fp16")]; + tensor input_191_cast_fp16 = add(x = out_29_cast_fp16, y = input_181_cast_fp16)[name = tensor("input_191_cast_fp16")]; + tensor input_193_cast_fp16 = relu(x = input_191_cast_fp16)[name = tensor("input_193_cast_fp16")]; + tensor input_195_pad_type_0 = const()[name = tensor("input_195_pad_type_0"), val = tensor("custom")]; + tensor input_195_pad_0 = const()[name = tensor("input_195_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_195_strides_0 = const()[name = tensor("input_195_strides_0"), val = tensor([1, 1])]; + tensor input_195_dilations_0 = const()[name = tensor("input_195_dilations_0"), val = tensor([1, 1])]; + tensor input_195_groups_0 = const()[name = tensor("input_195_groups_0"), val = tensor(1)]; + tensor const_73_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3122368))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3564800))), name = tensor("const_73_to_fp16_palettized"), shape = tensor([256, 256, 3, 3])]; + tensor const_74_to_fp16 = const()[name = tensor("const_74_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3564992)))]; + tensor input_197_cast_fp16 = conv(bias = const_74_to_fp16, dilations = input_195_dilations_0, groups = input_195_groups_0, pad = input_195_pad_0, pad_type = input_195_pad_type_0, strides = input_195_strides_0, weight = const_73_to_fp16_palettized, x = input_193_cast_fp16)[name = tensor("input_197_cast_fp16")]; + tensor input_199_cast_fp16 = relu(x = input_197_cast_fp16)[name = tensor("input_199_cast_fp16")]; + tensor input_201_pad_type_0 = const()[name = tensor("input_201_pad_type_0"), val = tensor("custom")]; + tensor input_201_pad_0 = const()[name = tensor("input_201_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_201_strides_0 = const()[name = tensor("input_201_strides_0"), val = tensor([1, 1])]; + tensor input_201_dilations_0 = const()[name = tensor("input_201_dilations_0"), val = tensor([1, 1])]; + tensor input_201_groups_0 = const()[name = tensor("input_201_groups_0"), val = tensor(1)]; + tensor const_75_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3565568))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4008000))), name = tensor("const_75_to_fp16_palettized"), shape = tensor([256, 256, 3, 3])]; + tensor const_76_to_fp16 = const()[name = tensor("const_76_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4008192)))]; + tensor out_cast_fp16 = conv(bias = const_76_to_fp16, dilations = input_201_dilations_0, groups = input_201_groups_0, pad = input_201_pad_0, pad_type = input_201_pad_type_0, strides = input_201_strides_0, weight = const_75_to_fp16_palettized, x = input_199_cast_fp16)[name = tensor("out_cast_fp16")]; + tensor input_203_cast_fp16 = add(x = out_cast_fp16, y = input_193_cast_fp16)[name = tensor("input_203_cast_fp16")]; + tensor x_cast_fp16 = relu(x = input_203_cast_fp16)[name = tensor("x_cast_fp16")]; + tensor var_577 = const()[name = tensor("op_577"), val = tensor([1, 2560, 125])]; + tensor sequences_cast_fp16 = reshape(shape = var_577, x = x_cast_fp16)[name = tensor("sequences_cast_fp16")]; + tensor expand_dims_0_axes_0 = const()[name = tensor("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0_cast_fp16 = expand_dims(axes = expand_dims_0_axes_0, x = speaker_masks)[name = tensor("expand_dims_0_cast_fp16")]; + tensor upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = tensor("upsample_nearest_neighbor_0_scale_factor_height_0"), val = tensor(0x1.b2a2a4p-3)]; + tensor upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = tensor("upsample_nearest_neighbor_0_scale_factor_width_0"), val = tensor(0x1p+0)]; + tensor upsample_nearest_neighbor_0_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0_cast_fp16)[name = tensor("upsample_nearest_neighbor_0_cast_fp16")]; + tensor weights_1_axes_0 = const()[name = tensor("weights_1_axes_0"), val = tensor([3])]; + tensor weights_1_cast_fp16 = squeeze(axes = weights_1_axes_0, x = upsample_nearest_neighbor_0_cast_fp16)[name = tensor("weights_1_cast_fp16")]; + tensor var_583_begin_0 = const()[name = tensor("op_583_begin_0"), val = tensor([0, 0, 0])]; + tensor var_583_end_0 = const()[name = tensor("op_583_end_0"), val = tensor([1, 1, 125])]; + tensor var_583_end_mask_0 = const()[name = tensor("op_583_end_mask_0"), val = tensor([true, false, true])]; + tensor var_583_squeeze_mask_0 = const()[name = tensor("op_583_squeeze_mask_0"), val = tensor([false, true, false])]; + tensor var_583_cast_fp16 = slice_by_index(begin = var_583_begin_0, end = var_583_end_0, end_mask = var_583_end_mask_0, squeeze_mask = var_583_squeeze_mask_0, x = weights_1_cast_fp16)[name = tensor("op_583_cast_fp16")]; + tensor weights_5_axes_0 = const()[name = tensor("weights_5_axes_0"), val = tensor([1])]; + tensor weights_5_cast_fp16 = expand_dims(axes = weights_5_axes_0, x = var_583_cast_fp16)[name = tensor("weights_5_cast_fp16")]; + tensor var_587_axes_0 = const()[name = tensor("op_587_axes_0"), val = tensor([2])]; + tensor var_587_keep_dims_0 = const()[name = tensor("op_587_keep_dims_0"), val = tensor(false)]; + tensor var_587_cast_fp16 = reduce_sum(axes = var_587_axes_0, keep_dims = var_587_keep_dims_0, x = weights_5_cast_fp16)[name = tensor("op_587_cast_fp16")]; + tensor var_588_to_fp16 = const()[name = tensor("op_588_to_fp16"), val = tensor(0x1p-24)]; + tensor v1_1_cast_fp16 = add(x = var_587_cast_fp16, y = var_588_to_fp16)[name = tensor("v1_1_cast_fp16")]; + tensor var_590_cast_fp16 = mul(x = sequences_cast_fp16, y = weights_5_cast_fp16)[name = tensor("op_590_cast_fp16")]; + tensor var_592_axes_0 = const()[name = tensor("op_592_axes_0"), val = tensor([2])]; + tensor var_592_keep_dims_0 = const()[name = tensor("op_592_keep_dims_0"), val = tensor(false)]; + tensor var_592_cast_fp16 = reduce_sum(axes = var_592_axes_0, keep_dims = var_592_keep_dims_0, x = var_590_cast_fp16)[name = tensor("op_592_cast_fp16")]; + tensor mean_1_cast_fp16 = real_div(x = var_592_cast_fp16, y = v1_1_cast_fp16)[name = tensor("mean_1_cast_fp16")]; + tensor var_594_axes_0 = const()[name = tensor("op_594_axes_0"), val = tensor([2])]; + tensor var_594_cast_fp16 = expand_dims(axes = var_594_axes_0, x = mean_1_cast_fp16)[name = tensor("op_594_cast_fp16")]; + tensor var_595_cast_fp16 = sub(x = sequences_cast_fp16, y = var_594_cast_fp16)[name = tensor("op_595_cast_fp16")]; + tensor dx2_1_cast_fp16 = mul(x = var_595_cast_fp16, y = var_595_cast_fp16)[name = tensor("dx2_1_cast_fp16")]; + tensor var_597_cast_fp16 = mul(x = weights_5_cast_fp16, y = weights_5_cast_fp16)[name = tensor("op_597_cast_fp16")]; + tensor v2_1_axes_0 = const()[name = tensor("v2_1_axes_0"), val = tensor([2])]; + tensor v2_1_keep_dims_0 = const()[name = tensor("v2_1_keep_dims_0"), val = tensor(false)]; + tensor v2_1_cast_fp16 = reduce_sum(axes = v2_1_axes_0, keep_dims = v2_1_keep_dims_0, x = var_597_cast_fp16)[name = tensor("v2_1_cast_fp16")]; + tensor var_600_cast_fp16 = mul(x = dx2_1_cast_fp16, y = weights_5_cast_fp16)[name = tensor("op_600_cast_fp16")]; + tensor var_602_axes_0 = const()[name = tensor("op_602_axes_0"), val = tensor([2])]; + tensor var_602_keep_dims_0 = const()[name = tensor("op_602_keep_dims_0"), val = tensor(false)]; + tensor var_602_cast_fp16 = reduce_sum(axes = var_602_axes_0, keep_dims = var_602_keep_dims_0, x = var_600_cast_fp16)[name = tensor("op_602_cast_fp16")]; + tensor var_603_cast_fp16 = real_div(x = v2_1_cast_fp16, y = v1_1_cast_fp16)[name = tensor("op_603_cast_fp16")]; + tensor var_604_cast_fp16 = sub(x = v1_1_cast_fp16, y = var_603_cast_fp16)[name = tensor("op_604_cast_fp16")]; + tensor var_605_to_fp16 = const()[name = tensor("op_605_to_fp16"), val = tensor(0x1p-24)]; + tensor var_606_cast_fp16 = add(x = var_604_cast_fp16, y = var_605_to_fp16)[name = tensor("op_606_cast_fp16")]; + tensor var_1_cast_fp16 = real_div(x = var_602_cast_fp16, y = var_606_cast_fp16)[name = tensor("var_1_cast_fp16")]; + tensor std_1_cast_fp16 = sqrt(x = var_1_cast_fp16)[name = tensor("std_1_cast_fp16")]; + tensor var_610_interleave_0 = const()[name = tensor("op_610_interleave_0"), val = tensor(false)]; + tensor var_610_cast_fp16 = concat(axis = var_12, interleave = var_610_interleave_0, values = (mean_1_cast_fp16, std_1_cast_fp16))[name = tensor("op_610_cast_fp16")]; + tensor var_612_begin_0 = const()[name = tensor("op_612_begin_0"), val = tensor([0, 1, 0])]; + tensor var_612_end_0 = const()[name = tensor("op_612_end_0"), val = tensor([1, 2, 125])]; + tensor var_612_end_mask_0 = const()[name = tensor("op_612_end_mask_0"), val = tensor([true, false, true])]; + tensor var_612_squeeze_mask_0 = const()[name = tensor("op_612_squeeze_mask_0"), val = tensor([false, true, false])]; + tensor var_612_cast_fp16 = slice_by_index(begin = var_612_begin_0, end = var_612_end_0, end_mask = var_612_end_mask_0, squeeze_mask = var_612_squeeze_mask_0, x = weights_1_cast_fp16)[name = tensor("op_612_cast_fp16")]; + tensor weights_9_axes_0 = const()[name = tensor("weights_9_axes_0"), val = tensor([1])]; + tensor weights_9_cast_fp16 = expand_dims(axes = weights_9_axes_0, x = var_612_cast_fp16)[name = tensor("weights_9_cast_fp16")]; + tensor var_616_axes_0 = const()[name = tensor("op_616_axes_0"), val = tensor([2])]; + tensor var_616_keep_dims_0 = const()[name = tensor("op_616_keep_dims_0"), val = tensor(false)]; + tensor var_616_cast_fp16 = reduce_sum(axes = var_616_axes_0, keep_dims = var_616_keep_dims_0, x = weights_9_cast_fp16)[name = tensor("op_616_cast_fp16")]; + tensor var_617_to_fp16 = const()[name = tensor("op_617_to_fp16"), val = tensor(0x1p-24)]; + tensor v1_3_cast_fp16 = add(x = var_616_cast_fp16, y = var_617_to_fp16)[name = tensor("v1_3_cast_fp16")]; + tensor var_619_cast_fp16 = mul(x = sequences_cast_fp16, y = weights_9_cast_fp16)[name = tensor("op_619_cast_fp16")]; + tensor var_621_axes_0 = const()[name = tensor("op_621_axes_0"), val = tensor([2])]; + tensor var_621_keep_dims_0 = const()[name = tensor("op_621_keep_dims_0"), val = tensor(false)]; + tensor var_621_cast_fp16 = reduce_sum(axes = var_621_axes_0, keep_dims = var_621_keep_dims_0, x = var_619_cast_fp16)[name = tensor("op_621_cast_fp16")]; + tensor mean_3_cast_fp16 = real_div(x = var_621_cast_fp16, y = v1_3_cast_fp16)[name = tensor("mean_3_cast_fp16")]; + tensor var_623_axes_0 = const()[name = tensor("op_623_axes_0"), val = tensor([2])]; + tensor var_623_cast_fp16 = expand_dims(axes = var_623_axes_0, x = mean_3_cast_fp16)[name = tensor("op_623_cast_fp16")]; + tensor var_624_cast_fp16 = sub(x = sequences_cast_fp16, y = var_623_cast_fp16)[name = tensor("op_624_cast_fp16")]; + tensor dx2_3_cast_fp16 = mul(x = var_624_cast_fp16, y = var_624_cast_fp16)[name = tensor("dx2_3_cast_fp16")]; + tensor var_626_cast_fp16 = mul(x = weights_9_cast_fp16, y = weights_9_cast_fp16)[name = tensor("op_626_cast_fp16")]; + tensor v2_3_axes_0 = const()[name = tensor("v2_3_axes_0"), val = tensor([2])]; + tensor v2_3_keep_dims_0 = const()[name = tensor("v2_3_keep_dims_0"), val = tensor(false)]; + tensor v2_3_cast_fp16 = reduce_sum(axes = v2_3_axes_0, keep_dims = v2_3_keep_dims_0, x = var_626_cast_fp16)[name = tensor("v2_3_cast_fp16")]; + tensor var_629_cast_fp16 = mul(x = dx2_3_cast_fp16, y = weights_9_cast_fp16)[name = tensor("op_629_cast_fp16")]; + tensor var_631_axes_0 = const()[name = tensor("op_631_axes_0"), val = tensor([2])]; + tensor var_631_keep_dims_0 = const()[name = tensor("op_631_keep_dims_0"), val = tensor(false)]; + tensor var_631_cast_fp16 = reduce_sum(axes = var_631_axes_0, keep_dims = var_631_keep_dims_0, x = var_629_cast_fp16)[name = tensor("op_631_cast_fp16")]; + tensor var_632_cast_fp16 = real_div(x = v2_3_cast_fp16, y = v1_3_cast_fp16)[name = tensor("op_632_cast_fp16")]; + tensor var_633_cast_fp16 = sub(x = v1_3_cast_fp16, y = var_632_cast_fp16)[name = tensor("op_633_cast_fp16")]; + tensor var_634_to_fp16 = const()[name = tensor("op_634_to_fp16"), val = tensor(0x1p-24)]; + tensor var_635_cast_fp16 = add(x = var_633_cast_fp16, y = var_634_to_fp16)[name = tensor("op_635_cast_fp16")]; + tensor var_3_cast_fp16 = real_div(x = var_631_cast_fp16, y = var_635_cast_fp16)[name = tensor("var_3_cast_fp16")]; + tensor std_3_cast_fp16 = sqrt(x = var_3_cast_fp16)[name = tensor("std_3_cast_fp16")]; + tensor var_639_interleave_0 = const()[name = tensor("op_639_interleave_0"), val = tensor(false)]; + tensor var_639_cast_fp16 = concat(axis = var_12, interleave = var_639_interleave_0, values = (mean_3_cast_fp16, std_3_cast_fp16))[name = tensor("op_639_cast_fp16")]; + tensor var_641_begin_0 = const()[name = tensor("op_641_begin_0"), val = tensor([0, 2, 0])]; + tensor var_641_end_0 = const()[name = tensor("op_641_end_0"), val = tensor([1, 3, 125])]; + tensor var_641_end_mask_0 = const()[name = tensor("op_641_end_mask_0"), val = tensor([true, false, true])]; + tensor var_641_squeeze_mask_0 = const()[name = tensor("op_641_squeeze_mask_0"), val = tensor([false, true, false])]; + tensor var_641_cast_fp16 = slice_by_index(begin = var_641_begin_0, end = var_641_end_0, end_mask = var_641_end_mask_0, squeeze_mask = var_641_squeeze_mask_0, x = weights_1_cast_fp16)[name = tensor("op_641_cast_fp16")]; + tensor weights_axes_0 = const()[name = tensor("weights_axes_0"), val = tensor([1])]; + tensor weights_cast_fp16 = expand_dims(axes = weights_axes_0, x = var_641_cast_fp16)[name = tensor("weights_cast_fp16")]; + tensor var_645_axes_0 = const()[name = tensor("op_645_axes_0"), val = tensor([2])]; + tensor var_645_keep_dims_0 = const()[name = tensor("op_645_keep_dims_0"), val = tensor(false)]; + tensor var_645_cast_fp16 = reduce_sum(axes = var_645_axes_0, keep_dims = var_645_keep_dims_0, x = weights_cast_fp16)[name = tensor("op_645_cast_fp16")]; + tensor var_646_to_fp16 = const()[name = tensor("op_646_to_fp16"), val = tensor(0x1p-24)]; + tensor v1_cast_fp16 = add(x = var_645_cast_fp16, y = var_646_to_fp16)[name = tensor("v1_cast_fp16")]; + tensor var_648_cast_fp16 = mul(x = sequences_cast_fp16, y = weights_cast_fp16)[name = tensor("op_648_cast_fp16")]; + tensor var_650_axes_0 = const()[name = tensor("op_650_axes_0"), val = tensor([2])]; + tensor var_650_keep_dims_0 = const()[name = tensor("op_650_keep_dims_0"), val = tensor(false)]; + tensor var_650_cast_fp16 = reduce_sum(axes = var_650_axes_0, keep_dims = var_650_keep_dims_0, x = var_648_cast_fp16)[name = tensor("op_650_cast_fp16")]; + tensor mean_cast_fp16 = real_div(x = var_650_cast_fp16, y = v1_cast_fp16)[name = tensor("mean_cast_fp16")]; + tensor var_652_axes_0 = const()[name = tensor("op_652_axes_0"), val = tensor([2])]; + tensor var_652_cast_fp16 = expand_dims(axes = var_652_axes_0, x = mean_cast_fp16)[name = tensor("op_652_cast_fp16")]; + tensor var_653_cast_fp16 = sub(x = sequences_cast_fp16, y = var_652_cast_fp16)[name = tensor("op_653_cast_fp16")]; + tensor dx2_cast_fp16 = mul(x = var_653_cast_fp16, y = var_653_cast_fp16)[name = tensor("dx2_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = weights_cast_fp16, y = weights_cast_fp16)[name = tensor("op_655_cast_fp16")]; + tensor v2_axes_0 = const()[name = tensor("v2_axes_0"), val = tensor([2])]; + tensor v2_keep_dims_0 = const()[name = tensor("v2_keep_dims_0"), val = tensor(false)]; + tensor v2_cast_fp16 = reduce_sum(axes = v2_axes_0, keep_dims = v2_keep_dims_0, x = var_655_cast_fp16)[name = tensor("v2_cast_fp16")]; + tensor var_658_cast_fp16 = mul(x = dx2_cast_fp16, y = weights_cast_fp16)[name = tensor("op_658_cast_fp16")]; + tensor var_660_axes_0 = const()[name = tensor("op_660_axes_0"), val = tensor([2])]; + tensor var_660_keep_dims_0 = const()[name = tensor("op_660_keep_dims_0"), val = tensor(false)]; + tensor var_660_cast_fp16 = reduce_sum(axes = var_660_axes_0, keep_dims = var_660_keep_dims_0, x = var_658_cast_fp16)[name = tensor("op_660_cast_fp16")]; + tensor var_661_cast_fp16 = real_div(x = v2_cast_fp16, y = v1_cast_fp16)[name = tensor("op_661_cast_fp16")]; + tensor var_662_cast_fp16 = sub(x = v1_cast_fp16, y = var_661_cast_fp16)[name = tensor("op_662_cast_fp16")]; + tensor var_663_to_fp16 = const()[name = tensor("op_663_to_fp16"), val = tensor(0x1p-24)]; + tensor var_664_cast_fp16 = add(x = var_662_cast_fp16, y = var_663_to_fp16)[name = tensor("op_664_cast_fp16")]; + tensor var_cast_fp16 = real_div(x = var_660_cast_fp16, y = var_664_cast_fp16)[name = tensor("var_cast_fp16")]; + tensor std_cast_fp16 = sqrt(x = var_cast_fp16)[name = tensor("std_cast_fp16")]; + tensor var_668_interleave_0 = const()[name = tensor("op_668_interleave_0"), val = tensor(false)]; + tensor var_668_cast_fp16 = concat(axis = var_12, interleave = var_668_interleave_0, values = (mean_cast_fp16, std_cast_fp16))[name = tensor("op_668_cast_fp16")]; + tensor input_axis_0 = const()[name = tensor("input_axis_0"), val = tensor(1)]; + tensor input_cast_fp16 = stack(axis = input_axis_0, values = (var_610_cast_fp16, var_639_cast_fp16, var_668_cast_fp16))[name = tensor("input_cast_fp16")]; + tensor model_resnet_seg_1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4008768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4991872))), name = tensor("model_resnet_seg_1_weight_to_fp16_palettized"), shape = tensor([256, 5120])]; + tensor model_resnet_seg_1_bias_to_fp16 = const()[name = tensor("model_resnet_seg_1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4992064)))]; + tensor speaker_embeddings = linear(bias = model_resnet_seg_1_bias_to_fp16, weight = model_resnet_seg_1_weight_to_fp16_palettized, x = input_cast_fp16)[name = tensor("linear_0_cast_fp16")]; + } -> (speaker_embeddings); +} \ No newline at end of file diff --git a/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedder.mlmodelc/weights/weight.bin b/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..fee8c679f6d7463d11d7d89be025fd1d116d8868 --- /dev/null +++ b/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:60cbb0421b3878825a27359253869a44dcc0aa238d8a62a937d9365f169ed6cb +size 4992640 diff --git a/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedderPreprocessor.mlmodelc/analytics/coremldata.bin b/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedderPreprocessor.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..520300aa0cb8e55941dca9a5ec6b0f9e5fd29e0c --- /dev/null +++ b/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedderPreprocessor.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5dd1e6ea694479da669d42d9752db8ebffdc7582b80c90f06452e2ed1f72cf8f +size 243 diff --git a/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedderPreprocessor.mlmodelc/coremldata.bin b/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedderPreprocessor.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..d6befe7c0c8b313075ab7c8af040d5a6890d0395 --- /dev/null +++ b/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedderPreprocessor.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7b2c40c7ba306c196e738d8d4efce7e59234875e854553a072cc4f964f6cb91e +size 330 diff --git a/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedderPreprocessor.mlmodelc/metadata.json b/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedderPreprocessor.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..a396303cf677afb1e7d0415671cb90b092cca4c2 --- /dev/null +++ b/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedderPreprocessor.mlmodelc/metadata.json @@ -0,0 +1,77 @@ +[ + { + "metadataOutputVersion" : "3.0", + "storagePrecision" : "Float32", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 1 × 998 × 80)", + "shortDescription" : "", + "shape" : "[1, 998, 80]", + "name" : "preprocessor_output_1", + "type" : "MultiArray" + } + ], + "modelParameters" : [ + + ], + "specificationVersion" : 7, + "mlProgramOperationTypeHistogram" : { + "Ios16.cast" : 2, + "Ios16.mul" : 4, + "SliceByIndex" : 2, + "Transpose" : 2, + "SlidingWindows" : 1, + "Ios16.sub" : 3, + "Ios16.log" : 1, + "Ios16.reduceMean" : 2, + "Ios16.square" : 2, + "Squeeze" : 2, + "Ios16.matmul" : 2, + "Ios16.add" : 1, + "Ios16.linear" : 1, + "ExpandDims" : 4, + "Ios16.gather" : 2, + "Ios16.maximum" : 1, + "Identity" : 1, + "Pad" : 2 + }, + "computePrecision" : "Mixed (Float16, Float32, Int32)", + "isUpdatable" : "0", + "stateSchema" : [ + + ], + "availability" : { + "macOS" : "13.0", + "tvOS" : "16.0", + "visionOS" : "1.0", + "watchOS" : "9.0", + "iOS" : "16.0", + "macCatalyst" : "16.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "userDefinedMetadata" : { + "com.github.apple.coremltools.source_dialect" : "TorchScript", + "com.github.apple.coremltools.source" : "torch==2.5.1", + "com.github.apple.coremltools.version" : "8.1" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 1 × 160000)", + "shortDescription" : "", + "shape" : "[1, 160000]", + "name" : "waveforms", + "type" : "MultiArray" + } + ], + "generatedClassName" : "SpeakerEmbeddingPreprocessor", + "method" : "predict" + } +] \ No newline at end of file diff --git a/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedderPreprocessor.mlmodelc/model.mil b/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedderPreprocessor.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..3bdac730dbc6970716314c338a4e209fa1f86f21 --- /dev/null +++ b/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedderPreprocessor.mlmodelc/model.mil @@ -0,0 +1,90 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3401.3.1"}, {"coremlc-version", "3401.4.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.1"}})] +{ + func main(tensor waveforms) { + tensor cast_0_dtype_0 = const()[name = tensor("cast_0_dtype_0"), val = tensor("fp32")]; + tensor var_2_promoted = const()[name = tensor("op_2_promoted"), val = tensor(0x1p+15)]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = waveforms)[name = tensor("cast_11")]; + tensor waveform_1 = mul(x = cast_0, y = var_2_promoted)[name = tensor("waveform_1")]; + tensor var_6_begin_0 = const()[name = tensor("op_6_begin_0"), val = tensor([0, 0])]; + tensor var_6_end_0 = const()[name = tensor("op_6_end_0"), val = tensor([1, 160000])]; + tensor var_6_end_mask_0 = const()[name = tensor("op_6_end_mask_0"), val = tensor([false, true])]; + tensor var_6_squeeze_mask_0 = const()[name = tensor("op_6_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_6 = slice_by_index(begin = var_6_begin_0, end = var_6_end_0, end_mask = var_6_end_mask_0, squeeze_mask = var_6_squeeze_mask_0, x = waveform_1)[name = tensor("op_6")]; + tensor sliding_windows_0_axis_0 = const()[name = tensor("sliding_windows_0_axis_0"), val = tensor(0)]; + tensor sliding_windows_0_size_0 = const()[name = tensor("sliding_windows_0_size_0"), val = tensor(400)]; + tensor sliding_windows_0_stride_0 = const()[name = tensor("sliding_windows_0_stride_0"), val = tensor(160)]; + tensor sliding_windows_0 = sliding_windows(axis = sliding_windows_0_axis_0, size = sliding_windows_0_size_0, stride = sliding_windows_0_stride_0, x = var_6)[name = tensor("sliding_windows_0")]; + tensor var_42_axes_0 = const()[name = tensor("op_42_axes_0"), val = tensor([1])]; + tensor var_42_keep_dims_0 = const()[name = tensor("op_42_keep_dims_0"), val = tensor(false)]; + tensor var_42 = reduce_mean(axes = var_42_axes_0, keep_dims = var_42_keep_dims_0, x = sliding_windows_0)[name = tensor("op_42")]; + tensor row_means_axes_0 = const()[name = tensor("row_means_axes_0"), val = tensor([1])]; + tensor row_means = expand_dims(axes = row_means_axes_0, x = var_42)[name = tensor("row_means")]; + tensor strided_input_3 = sub(x = sliding_windows_0, y = row_means)[name = tensor("strided_input_3")]; + tensor input_1_axes_0 = const()[name = tensor("input_1_axes_0"), val = tensor([0])]; + tensor input_1 = expand_dims(axes = input_1_axes_0, x = strided_input_3)[name = tensor("input_1")]; + tensor const_2 = const()[name = tensor("const_2"), val = tensor(0x0p+0)]; + tensor var_54_pad_0 = const()[name = tensor("op_54_pad_0"), val = tensor([0, 0, 0, 0, 1, 0])]; + tensor var_54_mode_0 = const()[name = tensor("op_54_mode_0"), val = tensor("replicate")]; + tensor var_54 = pad(constant_val = const_2, mode = var_54_mode_0, pad = var_54_pad_0, x = input_1)[name = tensor("op_54")]; + tensor offset_strided_input_axes_0 = const()[name = tensor("offset_strided_input_axes_0"), val = tensor([0])]; + tensor offset_strided_input = squeeze(axes = offset_strided_input_axes_0, x = var_54)[name = tensor("offset_strided_input")]; + tensor var_66_begin_0 = const()[name = tensor("op_66_begin_0"), val = tensor([0, 0])]; + tensor var_66_end_0 = const()[name = tensor("op_66_end_0"), val = tensor([998, 400])]; + tensor var_66_end_mask_0 = const()[name = tensor("op_66_end_mask_0"), val = tensor([true, false])]; + tensor var_66 = slice_by_index(begin = var_66_begin_0, end = var_66_end_0, end_mask = var_66_end_mask_0, x = offset_strided_input)[name = tensor("op_66")]; + tensor var_67 = const()[name = tensor("op_67"), val = tensor(0x1.f0a3d8p-1)]; + tensor var_68 = mul(x = var_66, y = var_67)[name = tensor("op_68")]; + tensor strided_input_5 = sub(x = strided_input_3, y = var_68)[name = tensor("strided_input_5")]; + tensor window_function = const()[name = tensor("window_function"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor strided_input_7 = mul(x = strided_input_5, y = window_function)[name = tensor("strided_input_7")]; + tensor input_3_axes_0 = const()[name = tensor("input_3_axes_0"), val = tensor([0])]; + tensor input_3 = expand_dims(axes = input_3_axes_0, x = strided_input_7)[name = tensor("input_3")]; + tensor const_3 = const()[name = tensor("const_3"), val = tensor(0x0p+0)]; + tensor var_90_pad_0 = const()[name = tensor("op_90_pad_0"), val = tensor([0, 0, 0, 0, 0, 112])]; + tensor var_90_mode_0 = const()[name = tensor("op_90_mode_0"), val = tensor("constant")]; + tensor var_90 = pad(constant_val = const_3, mode = var_90_mode_0, pad = var_90_pad_0, x = input_3)[name = tensor("op_90")]; + tensor strided_input_axes_0 = const()[name = tensor("strided_input_axes_0"), val = tensor([0])]; + tensor strided_input = squeeze(axes = strided_input_axes_0, x = var_90)[name = tensor("strided_input")]; + tensor cos_0 = const()[name = tensor("cos_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1728)))]; + tensor sin_0 = const()[name = tensor("sin_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1050368)))]; + tensor matmul_1_transpose_x_1 = const()[name = tensor("matmul_1_transpose_x_1"), val = tensor(false)]; + tensor matmul_1_transpose_y_1 = const()[name = tensor("matmul_1_transpose_y_1"), val = tensor(true)]; + tensor matmul_1 = matmul(transpose_x = matmul_1_transpose_x_1, transpose_y = matmul_1_transpose_y_1, x = cos_0, y = strided_input)[name = tensor("matmul_1")]; + tensor matmul_3_transpose_x_1 = const()[name = tensor("matmul_3_transpose_x_1"), val = tensor(false)]; + tensor matmul_3_transpose_y_1 = const()[name = tensor("matmul_3_transpose_y_1"), val = tensor(true)]; + tensor matmul_3 = matmul(transpose_x = matmul_3_transpose_x_1, transpose_y = matmul_3_transpose_y_1, x = sin_0, y = strided_input)[name = tensor("matmul_3")]; + tensor mul_1_y_0 = const()[name = tensor("mul_1_y_0"), val = tensor(-0x1p+0)]; + tensor mul_1 = mul(x = matmul_3, y = mul_1_y_0)[name = tensor("mul_1")]; + tensor transpose_3_perm_0 = const()[name = tensor("transpose_3_perm_0"), val = tensor([-1, 0])]; + tensor transpose_4_perm_0 = const()[name = tensor("transpose_4_perm_0"), val = tensor([-1, 0])]; + tensor range_1d_2 = const()[name = tensor("range_1d_2"), val = tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256])]; + tensor gather_0_axis_0 = const()[name = tensor("gather_0_axis_0"), val = tensor(-1)]; + tensor gather_0_batch_dims_0 = const()[name = tensor("gather_0_batch_dims_0"), val = tensor(0)]; + tensor transpose_3 = transpose(perm = transpose_3_perm_0, x = matmul_1)[name = tensor("transpose_6")]; + tensor gather_0 = gather(axis = gather_0_axis_0, batch_dims = gather_0_batch_dims_0, indices = range_1d_2, x = transpose_3)[name = tensor("gather_0")]; + tensor gather_1_axis_0 = const()[name = tensor("gather_1_axis_0"), val = tensor(-1)]; + tensor gather_1_batch_dims_0 = const()[name = tensor("gather_1_batch_dims_0"), val = tensor(0)]; + tensor transpose_4 = transpose(perm = transpose_4_perm_0, x = mul_1)[name = tensor("transpose_5")]; + tensor gather_1 = gather(axis = gather_1_axis_0, batch_dims = gather_1_batch_dims_0, indices = range_1d_2, x = transpose_4)[name = tensor("gather_1")]; + tensor square_0 = square(x = gather_0)[name = tensor("square_0")]; + tensor square_1 = square(x = gather_1)[name = tensor("square_1")]; + tensor add_1 = add(x = square_0, y = square_1)[name = tensor("add_1")]; + tensor spectrum = identity(x = add_1)[name = tensor("spectrum")]; + tensor mel_energies_3 = const()[name = tensor("mel_energies_3"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2099008)))]; + tensor mel_energies_bias_0 = const()[name = tensor("mel_energies_bias_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2181312)))]; + tensor mel_energies = linear(bias = mel_energies_bias_0, weight = mel_energies_3, x = spectrum)[name = tensor("mel_energies")]; + tensor const_10 = const()[name = tensor("const_10"), val = tensor(0x1p-23)]; + tensor var_186 = maximum(x = mel_energies, y = const_10)[name = tensor("op_186")]; + tensor filter_banks_epsilon_0 = const()[name = tensor("filter_banks_epsilon_0"), val = tensor(0x1p-149)]; + tensor filter_banks = log(epsilon = filter_banks_epsilon_0, x = var_186)[name = tensor("filter_banks")]; + tensor var_192_axes_0 = const()[name = tensor("op_192_axes_0"), val = tensor([0])]; + tensor var_192_keep_dims_0 = const()[name = tensor("op_192_keep_dims_0"), val = tensor(true)]; + tensor var_192 = reduce_mean(axes = var_192_axes_0, keep_dims = var_192_keep_dims_0, x = filter_banks)[name = tensor("op_192")]; + tensor var_194 = sub(x = filter_banks, y = var_192)[name = tensor("op_194")]; + tensor obj_axes_0 = const()[name = tensor("obj_axes_0"), val = tensor([0])]; + tensor preprocessor_output_1_type_fp32 = expand_dims(axes = obj_axes_0, x = var_194)[name = tensor("obj")]; + tensor cast_9_dtype_0 = const()[name = tensor("cast_9_dtype_0"), val = tensor("fp16")]; + tensor preprocessor_output_1 = cast(dtype = cast_9_dtype_0, x = preprocessor_output_1_type_fp32)[name = tensor("cast_10")]; + } -> (preprocessor_output_1); +} \ No newline at end of file diff --git a/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedderPreprocessor.mlmodelc/weights/weight.bin b/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedderPreprocessor.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..a370d87e4d244f921e8f79574bc5385907b9bb29 --- /dev/null +++ b/speaker_embedder/pyannote-v3/W6A16/SpeakerEmbedderPreprocessor.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5f2c284bd22f1f7ab76901c1c6e57f82d4ebbf057fa0b924aad057f124f77a89 +size 2181696 diff --git a/speaker_embedder/pyannote-v3/W8A16/LICENSE_NOTICE.txt b/speaker_embedder/pyannote-v3/W8A16/LICENSE_NOTICE.txt new file mode 100644 index 0000000000000000000000000000000000000000..be2da6c6e6d746ab53f1b21eac16d611aed1193a --- /dev/null +++ b/speaker_embedder/pyannote-v3/W8A16/LICENSE_NOTICE.txt @@ -0,0 +1,7 @@ +Argmax proprietary and confidential. Under NDA. + +Copyright 2024 Argmax, Inc. All rights reserved. + +Unauthorized access, copying, use, distribution, and or commercialization of this file, via any medium or means is strictly prohibited. + +Please contact Argmax for licensing information at info@argmaxinc.com. diff --git a/speaker_embedder/pyannote-v3/W8A16/README.txt b/speaker_embedder/pyannote-v3/W8A16/README.txt new file mode 100644 index 0000000000000000000000000000000000000000..38ee63eaf9af8162550ccc0aacba367b59375792 --- /dev/null +++ b/speaker_embedder/pyannote-v3/W8A16/README.txt @@ -0,0 +1,6 @@ +# License + +Original model weights: https://github.com/wenet-e2e/wespeaker/blob/master/docs/pretrained.md#model-license +Argmax-optimized model asset (Assets with `.mlmodelc` extension): https://huggingface.co/argmaxinc/speakerkit-pro/blob/main/LICENSE_NOTICE.txt + +Please contact info@argmaxinc.com for licensing SpeakerKit Pro assets \ No newline at end of file diff --git a/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedder.mlmodelc/analytics/coremldata.bin b/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedder.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..25055201c1c2d3d681d4257d64367db9f38c684a --- /dev/null +++ b/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedder.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0a06be051bd0fe5990b1778044af9d0d5ab0af40f9867cb190b6384ff6417619 +size 243 diff --git a/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedder.mlmodelc/coremldata.bin b/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedder.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..b0871551dfc913ef3f5880289571e9e93f9cb888 --- /dev/null +++ b/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedder.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c354879615fe262bc5f8c92b69df8c58111d06331c5d20ddb2e0efe99ea4441c +size 370 diff --git a/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedder.mlmodelc/metadata.json b/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedder.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..e7a1e31741da659ee0606fd574b5cd77cf990dac --- /dev/null +++ b/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedder.mlmodelc/metadata.json @@ -0,0 +1,87 @@ +[ + { + "metadataOutputVersion" : "3.0", + "storagePrecision" : "Mixed (Float16, Palettized (8 bits))", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 1 × 3 × 256)", + "shortDescription" : "", + "shape" : "[1, 3, 256]", + "name" : "speaker_embeddings", + "type" : "MultiArray" + } + ], + "modelParameters" : [ + + ], + "specificationVersion" : 7, + "mlProgramOperationTypeHistogram" : { + "Concat" : 3, + "Ios16.mul" : 12, + "SliceByIndex" : 3, + "Ios16.constexprLutToDense" : 35, + "Transpose" : 1, + "Ios16.sub" : 6, + "Ios16.sqrt" : 3, + "Stack" : 1, + "UpsampleNearestNeighbor" : 1, + "Ios16.conv" : 36, + "Ios16.add" : 22, + "Squeeze" : 1, + "Ios16.relu" : 33, + "Ios16.realDiv" : 9, + "Ios16.reduceSum" : 12, + "ExpandDims" : 8, + "Ios16.linear" : 1, + "Ios16.reshape" : 1 + }, + "computePrecision" : "Mixed (Float16, Float32, Int32)", + "isUpdatable" : "0", + "stateSchema" : [ + + ], + "availability" : { + "macOS" : "13.0", + "tvOS" : "16.0", + "visionOS" : "1.0", + "watchOS" : "9.0", + "iOS" : "16.0", + "macCatalyst" : "16.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "userDefinedMetadata" : { + "com.github.apple.coremltools.source_dialect" : "TorchScript", + "com.github.apple.coremltools.source" : "torch==2.5.1", + "com.github.apple.coremltools.version" : "8.1" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 1 × 998 × 80)", + "shortDescription" : "", + "shape" : "[1, 998, 80]", + "name" : "preprocessor_output_1", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 1 × 3 × 589)", + "shortDescription" : "", + "shape" : "[1, 3, 589]", + "name" : "speaker_masks", + "type" : "MultiArray" + } + ], + "generatedClassName" : "SpeakerEmbedding_8_bit", + "method" : "predict" + } +] \ No newline at end of file diff --git a/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedder.mlmodelc/model.mil b/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedder.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..8ffb5e9eea4aa8d4f18e5f7da9c192dfb1e8011f --- /dev/null +++ b/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedder.mlmodelc/model.mil @@ -0,0 +1,473 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3401.3.1"}, {"coremlc-version", "3401.4.1"}})] +{ + func main(tensor preprocessor_output_1, tensor speaker_masks) { + tensor var_12 = const()[name = tensor("op_12"), val = tensor(1)]; + tensor var_22 = const()[name = tensor("op_22"), val = tensor([0, 2, 1])]; + tensor input_1_axes_0 = const()[name = tensor("input_1_axes_0"), val = tensor([1])]; + tensor fbank_cast_fp16 = transpose(perm = var_22, x = preprocessor_output_1)[name = tensor("transpose_0")]; + tensor input_1_cast_fp16 = expand_dims(axes = input_1_axes_0, x = fbank_cast_fp16)[name = tensor("input_1_cast_fp16")]; + tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("custom")]; + tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_3_strides_0 = const()[name = tensor("input_3_strides_0"), val = tensor([1, 1])]; + tensor input_3_dilations_0 = const()[name = tensor("input_3_dilations_0"), val = tensor([1, 1])]; + tensor input_3_groups_0 = const()[name = tensor("input_3_groups_0"), val = tensor(1)]; + tensor const_5_to_fp16 = const()[name = tensor("const_5_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor const_6_to_fp16 = const()[name = tensor("const_6_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(704)))]; + tensor input_5_cast_fp16 = conv(bias = const_6_to_fp16, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = const_5_to_fp16, x = input_1_cast_fp16)[name = tensor("input_5_cast_fp16")]; + tensor input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; + tensor input_9_pad_type_0 = const()[name = tensor("input_9_pad_type_0"), val = tensor("custom")]; + tensor input_9_pad_0 = const()[name = tensor("input_9_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_9_strides_0 = const()[name = tensor("input_9_strides_0"), val = tensor([1, 1])]; + tensor input_9_dilations_0 = const()[name = tensor("input_9_dilations_0"), val = tensor([1, 1])]; + tensor input_9_groups_0 = const()[name = tensor("input_9_groups_0"), val = tensor(1)]; + tensor const_7_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10112))), name = tensor("const_7_to_fp16_palettized"), shape = tensor([32, 32, 3, 3])]; + tensor const_8_to_fp16 = const()[name = tensor("const_8_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10688)))]; + tensor input_11_cast_fp16 = conv(bias = const_8_to_fp16, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = const_7_to_fp16_palettized, x = input_7_cast_fp16)[name = tensor("input_11_cast_fp16")]; + tensor input_13_cast_fp16 = relu(x = input_11_cast_fp16)[name = tensor("input_13_cast_fp16")]; + tensor input_15_pad_type_0 = const()[name = tensor("input_15_pad_type_0"), val = tensor("custom")]; + tensor input_15_pad_0 = const()[name = tensor("input_15_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_15_strides_0 = const()[name = tensor("input_15_strides_0"), val = tensor([1, 1])]; + tensor input_15_dilations_0 = const()[name = tensor("input_15_dilations_0"), val = tensor([1, 1])]; + tensor input_15_groups_0 = const()[name = tensor("input_15_groups_0"), val = tensor(1)]; + tensor const_9_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20096))), name = tensor("const_9_to_fp16_palettized"), shape = tensor([32, 32, 3, 3])]; + tensor const_10_to_fp16 = const()[name = tensor("const_10_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20672)))]; + tensor out_1_cast_fp16 = conv(bias = const_10_to_fp16, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = const_9_to_fp16_palettized, x = input_13_cast_fp16)[name = tensor("out_1_cast_fp16")]; + tensor input_17_cast_fp16 = add(x = out_1_cast_fp16, y = input_7_cast_fp16)[name = tensor("input_17_cast_fp16")]; + tensor input_19_cast_fp16 = relu(x = input_17_cast_fp16)[name = tensor("input_19_cast_fp16")]; + tensor input_21_pad_type_0 = const()[name = tensor("input_21_pad_type_0"), val = tensor("custom")]; + tensor input_21_pad_0 = const()[name = tensor("input_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_21_strides_0 = const()[name = tensor("input_21_strides_0"), val = tensor([1, 1])]; + tensor input_21_dilations_0 = const()[name = tensor("input_21_dilations_0"), val = tensor([1, 1])]; + tensor input_21_groups_0 = const()[name = tensor("input_21_groups_0"), val = tensor(1)]; + tensor const_11_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30080))), name = tensor("const_11_to_fp16_palettized"), shape = tensor([32, 32, 3, 3])]; + tensor const_12_to_fp16 = const()[name = tensor("const_12_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30656)))]; + tensor input_23_cast_fp16 = conv(bias = const_12_to_fp16, dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = const_11_to_fp16_palettized, x = input_19_cast_fp16)[name = tensor("input_23_cast_fp16")]; + tensor input_25_cast_fp16 = relu(x = input_23_cast_fp16)[name = tensor("input_25_cast_fp16")]; + tensor input_27_pad_type_0 = const()[name = tensor("input_27_pad_type_0"), val = tensor("custom")]; + tensor input_27_pad_0 = const()[name = tensor("input_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_27_strides_0 = const()[name = tensor("input_27_strides_0"), val = tensor([1, 1])]; + tensor input_27_dilations_0 = const()[name = tensor("input_27_dilations_0"), val = tensor([1, 1])]; + tensor input_27_groups_0 = const()[name = tensor("input_27_groups_0"), val = tensor(1)]; + tensor const_13_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40064))), name = tensor("const_13_to_fp16_palettized"), shape = tensor([32, 32, 3, 3])]; + tensor const_14_to_fp16 = const()[name = tensor("const_14_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40640)))]; + tensor out_3_cast_fp16 = conv(bias = const_14_to_fp16, dilations = input_27_dilations_0, groups = input_27_groups_0, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = input_27_strides_0, weight = const_13_to_fp16_palettized, x = input_25_cast_fp16)[name = tensor("out_3_cast_fp16")]; + tensor input_29_cast_fp16 = add(x = out_3_cast_fp16, y = input_19_cast_fp16)[name = tensor("input_29_cast_fp16")]; + tensor input_31_cast_fp16 = relu(x = input_29_cast_fp16)[name = tensor("input_31_cast_fp16")]; + tensor input_33_pad_type_0 = const()[name = tensor("input_33_pad_type_0"), val = tensor("custom")]; + tensor input_33_pad_0 = const()[name = tensor("input_33_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_33_strides_0 = const()[name = tensor("input_33_strides_0"), val = tensor([1, 1])]; + tensor input_33_dilations_0 = const()[name = tensor("input_33_dilations_0"), val = tensor([1, 1])]; + tensor input_33_groups_0 = const()[name = tensor("input_33_groups_0"), val = tensor(1)]; + tensor const_15_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50048))), name = tensor("const_15_to_fp16_palettized"), shape = tensor([32, 32, 3, 3])]; + tensor const_16_to_fp16 = const()[name = tensor("const_16_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50624)))]; + tensor input_35_cast_fp16 = conv(bias = const_16_to_fp16, dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = const_15_to_fp16_palettized, x = input_31_cast_fp16)[name = tensor("input_35_cast_fp16")]; + tensor input_37_cast_fp16 = relu(x = input_35_cast_fp16)[name = tensor("input_37_cast_fp16")]; + tensor input_39_pad_type_0 = const()[name = tensor("input_39_pad_type_0"), val = tensor("custom")]; + tensor input_39_pad_0 = const()[name = tensor("input_39_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_39_strides_0 = const()[name = tensor("input_39_strides_0"), val = tensor([1, 1])]; + tensor input_39_dilations_0 = const()[name = tensor("input_39_dilations_0"), val = tensor([1, 1])]; + tensor input_39_groups_0 = const()[name = tensor("input_39_groups_0"), val = tensor(1)]; + tensor const_17_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50752))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60032))), name = tensor("const_17_to_fp16_palettized"), shape = tensor([32, 32, 3, 3])]; + tensor const_18_to_fp16 = const()[name = tensor("const_18_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60608)))]; + tensor out_5_cast_fp16 = conv(bias = const_18_to_fp16, dilations = input_39_dilations_0, groups = input_39_groups_0, pad = input_39_pad_0, pad_type = input_39_pad_type_0, strides = input_39_strides_0, weight = const_17_to_fp16_palettized, x = input_37_cast_fp16)[name = tensor("out_5_cast_fp16")]; + tensor input_41_cast_fp16 = add(x = out_5_cast_fp16, y = input_31_cast_fp16)[name = tensor("input_41_cast_fp16")]; + tensor input_43_cast_fp16 = relu(x = input_41_cast_fp16)[name = tensor("input_43_cast_fp16")]; + tensor input_45_pad_type_0 = const()[name = tensor("input_45_pad_type_0"), val = tensor("custom")]; + tensor input_45_pad_0 = const()[name = tensor("input_45_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_45_strides_0 = const()[name = tensor("input_45_strides_0"), val = tensor([2, 2])]; + tensor input_45_dilations_0 = const()[name = tensor("input_45_dilations_0"), val = tensor([1, 1])]; + tensor input_45_groups_0 = const()[name = tensor("input_45_groups_0"), val = tensor(1)]; + tensor const_19_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79232))), name = tensor("const_19_to_fp16_palettized"), shape = tensor([64, 32, 3, 3])]; + tensor const_20_to_fp16 = const()[name = tensor("const_20_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79808)))]; + tensor input_47_cast_fp16 = conv(bias = const_20_to_fp16, dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = const_19_to_fp16_palettized, x = input_43_cast_fp16)[name = tensor("input_47_cast_fp16")]; + tensor input_49_cast_fp16 = relu(x = input_47_cast_fp16)[name = tensor("input_49_cast_fp16")]; + tensor input_51_pad_type_0 = const()[name = tensor("input_51_pad_type_0"), val = tensor("custom")]; + tensor input_51_pad_0 = const()[name = tensor("input_51_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_51_strides_0 = const()[name = tensor("input_51_strides_0"), val = tensor([1, 1])]; + tensor input_51_dilations_0 = const()[name = tensor("input_51_dilations_0"), val = tensor([1, 1])]; + tensor input_51_groups_0 = const()[name = tensor("input_51_groups_0"), val = tensor(1)]; + tensor const_21_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80000))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(116928))), name = tensor("const_21_to_fp16_palettized"), shape = tensor([64, 64, 3, 3])]; + tensor const_22_to_fp16 = const()[name = tensor("const_22_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117504)))]; + tensor out_7_cast_fp16 = conv(bias = const_22_to_fp16, dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = const_21_to_fp16_palettized, x = input_49_cast_fp16)[name = tensor("out_7_cast_fp16")]; + tensor input_53_pad_type_0 = const()[name = tensor("input_53_pad_type_0"), val = tensor("valid")]; + tensor input_53_strides_0 = const()[name = tensor("input_53_strides_0"), val = tensor([2, 2])]; + tensor input_53_pad_0 = const()[name = tensor("input_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_53_dilations_0 = const()[name = tensor("input_53_dilations_0"), val = tensor([1, 1])]; + tensor input_53_groups_0 = const()[name = tensor("input_53_groups_0"), val = tensor(1)]; + tensor const_23_to_fp16 = const()[name = tensor("const_23_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117696)))]; + tensor const_24_to_fp16 = const()[name = tensor("const_24_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121856)))]; + tensor var_171_cast_fp16 = conv(bias = const_24_to_fp16, dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = const_23_to_fp16, x = input_43_cast_fp16)[name = tensor("op_171_cast_fp16")]; + tensor input_55_cast_fp16 = add(x = out_7_cast_fp16, y = var_171_cast_fp16)[name = tensor("input_55_cast_fp16")]; + tensor input_57_cast_fp16 = relu(x = input_55_cast_fp16)[name = tensor("input_57_cast_fp16")]; + tensor input_59_pad_type_0 = const()[name = tensor("input_59_pad_type_0"), val = tensor("custom")]; + tensor input_59_pad_0 = const()[name = tensor("input_59_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_59_strides_0 = const()[name = tensor("input_59_strides_0"), val = tensor([1, 1])]; + tensor input_59_dilations_0 = const()[name = tensor("input_59_dilations_0"), val = tensor([1, 1])]; + tensor input_59_groups_0 = const()[name = tensor("input_59_groups_0"), val = tensor(1)]; + tensor const_25_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158976))), name = tensor("const_25_to_fp16_palettized"), shape = tensor([64, 64, 3, 3])]; + tensor const_26_to_fp16 = const()[name = tensor("const_26_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159552)))]; + tensor input_61_cast_fp16 = conv(bias = const_26_to_fp16, dilations = input_59_dilations_0, groups = input_59_groups_0, pad = input_59_pad_0, pad_type = input_59_pad_type_0, strides = input_59_strides_0, weight = const_25_to_fp16_palettized, x = input_57_cast_fp16)[name = tensor("input_61_cast_fp16")]; + tensor input_63_cast_fp16 = relu(x = input_61_cast_fp16)[name = tensor("input_63_cast_fp16")]; + tensor input_65_pad_type_0 = const()[name = tensor("input_65_pad_type_0"), val = tensor("custom")]; + tensor input_65_pad_0 = const()[name = tensor("input_65_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_65_strides_0 = const()[name = tensor("input_65_strides_0"), val = tensor([1, 1])]; + tensor input_65_dilations_0 = const()[name = tensor("input_65_dilations_0"), val = tensor([1, 1])]; + tensor input_65_groups_0 = const()[name = tensor("input_65_groups_0"), val = tensor(1)]; + tensor const_27_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(159744))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196672))), name = tensor("const_27_to_fp16_palettized"), shape = tensor([64, 64, 3, 3])]; + tensor const_28_to_fp16 = const()[name = tensor("const_28_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197248)))]; + tensor out_9_cast_fp16 = conv(bias = const_28_to_fp16, dilations = input_65_dilations_0, groups = input_65_groups_0, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = input_65_strides_0, weight = const_27_to_fp16_palettized, x = input_63_cast_fp16)[name = tensor("out_9_cast_fp16")]; + tensor input_67_cast_fp16 = add(x = out_9_cast_fp16, y = input_57_cast_fp16)[name = tensor("input_67_cast_fp16")]; + tensor input_69_cast_fp16 = relu(x = input_67_cast_fp16)[name = tensor("input_69_cast_fp16")]; + tensor input_71_pad_type_0 = const()[name = tensor("input_71_pad_type_0"), val = tensor("custom")]; + tensor input_71_pad_0 = const()[name = tensor("input_71_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_71_strides_0 = const()[name = tensor("input_71_strides_0"), val = tensor([1, 1])]; + tensor input_71_dilations_0 = const()[name = tensor("input_71_dilations_0"), val = tensor([1, 1])]; + tensor input_71_groups_0 = const()[name = tensor("input_71_groups_0"), val = tensor(1)]; + tensor const_29_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(234368))), name = tensor("const_29_to_fp16_palettized"), shape = tensor([64, 64, 3, 3])]; + tensor const_30_to_fp16 = const()[name = tensor("const_30_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(234944)))]; + tensor input_73_cast_fp16 = conv(bias = const_30_to_fp16, dilations = input_71_dilations_0, groups = input_71_groups_0, pad = input_71_pad_0, pad_type = input_71_pad_type_0, strides = input_71_strides_0, weight = const_29_to_fp16_palettized, x = input_69_cast_fp16)[name = tensor("input_73_cast_fp16")]; + tensor input_75_cast_fp16 = relu(x = input_73_cast_fp16)[name = tensor("input_75_cast_fp16")]; + tensor input_77_pad_type_0 = const()[name = tensor("input_77_pad_type_0"), val = tensor("custom")]; + tensor input_77_pad_0 = const()[name = tensor("input_77_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_77_strides_0 = const()[name = tensor("input_77_strides_0"), val = tensor([1, 1])]; + tensor input_77_dilations_0 = const()[name = tensor("input_77_dilations_0"), val = tensor([1, 1])]; + tensor input_77_groups_0 = const()[name = tensor("input_77_groups_0"), val = tensor(1)]; + tensor const_31_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(235136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272064))), name = tensor("const_31_to_fp16_palettized"), shape = tensor([64, 64, 3, 3])]; + tensor const_32_to_fp16 = const()[name = tensor("const_32_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272640)))]; + tensor out_11_cast_fp16 = conv(bias = const_32_to_fp16, dilations = input_77_dilations_0, groups = input_77_groups_0, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = input_77_strides_0, weight = const_31_to_fp16_palettized, x = input_75_cast_fp16)[name = tensor("out_11_cast_fp16")]; + tensor input_79_cast_fp16 = add(x = out_11_cast_fp16, y = input_69_cast_fp16)[name = tensor("input_79_cast_fp16")]; + tensor input_81_cast_fp16 = relu(x = input_79_cast_fp16)[name = tensor("input_81_cast_fp16")]; + tensor input_83_pad_type_0 = const()[name = tensor("input_83_pad_type_0"), val = tensor("custom")]; + tensor input_83_pad_0 = const()[name = tensor("input_83_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_83_strides_0 = const()[name = tensor("input_83_strides_0"), val = tensor([1, 1])]; + tensor input_83_dilations_0 = const()[name = tensor("input_83_dilations_0"), val = tensor([1, 1])]; + tensor input_83_groups_0 = const()[name = tensor("input_83_groups_0"), val = tensor(1)]; + tensor const_33_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(309760))), name = tensor("const_33_to_fp16_palettized"), shape = tensor([64, 64, 3, 3])]; + tensor const_34_to_fp16 = const()[name = tensor("const_34_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(310336)))]; + tensor input_85_cast_fp16 = conv(bias = const_34_to_fp16, dilations = input_83_dilations_0, groups = input_83_groups_0, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = input_83_strides_0, weight = const_33_to_fp16_palettized, x = input_81_cast_fp16)[name = tensor("input_85_cast_fp16")]; + tensor input_87_cast_fp16 = relu(x = input_85_cast_fp16)[name = tensor("input_87_cast_fp16")]; + tensor input_89_pad_type_0 = const()[name = tensor("input_89_pad_type_0"), val = tensor("custom")]; + tensor input_89_pad_0 = const()[name = tensor("input_89_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_89_strides_0 = const()[name = tensor("input_89_strides_0"), val = tensor([1, 1])]; + tensor input_89_dilations_0 = const()[name = tensor("input_89_dilations_0"), val = tensor([1, 1])]; + tensor input_89_groups_0 = const()[name = tensor("input_89_groups_0"), val = tensor(1)]; + tensor const_35_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(310528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(347456))), name = tensor("const_35_to_fp16_palettized"), shape = tensor([64, 64, 3, 3])]; + tensor const_36_to_fp16 = const()[name = tensor("const_36_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(348032)))]; + tensor out_13_cast_fp16 = conv(bias = const_36_to_fp16, dilations = input_89_dilations_0, groups = input_89_groups_0, pad = input_89_pad_0, pad_type = input_89_pad_type_0, strides = input_89_strides_0, weight = const_35_to_fp16_palettized, x = input_87_cast_fp16)[name = tensor("out_13_cast_fp16")]; + tensor input_91_cast_fp16 = add(x = out_13_cast_fp16, y = input_81_cast_fp16)[name = tensor("input_91_cast_fp16")]; + tensor input_93_cast_fp16 = relu(x = input_91_cast_fp16)[name = tensor("input_93_cast_fp16")]; + tensor input_95_pad_type_0 = const()[name = tensor("input_95_pad_type_0"), val = tensor("custom")]; + tensor input_95_pad_0 = const()[name = tensor("input_95_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_95_strides_0 = const()[name = tensor("input_95_strides_0"), val = tensor([2, 2])]; + tensor input_95_dilations_0 = const()[name = tensor("input_95_dilations_0"), val = tensor([1, 1])]; + tensor input_95_groups_0 = const()[name = tensor("input_95_groups_0"), val = tensor(1)]; + tensor const_37_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(348224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(422016))), name = tensor("const_37_to_fp16_palettized"), shape = tensor([128, 64, 3, 3])]; + tensor const_38_to_fp16 = const()[name = tensor("const_38_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(422592)))]; + tensor input_97_cast_fp16 = conv(bias = const_38_to_fp16, dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = const_37_to_fp16_palettized, x = input_93_cast_fp16)[name = tensor("input_97_cast_fp16")]; + tensor input_99_cast_fp16 = relu(x = input_97_cast_fp16)[name = tensor("input_99_cast_fp16")]; + tensor input_101_pad_type_0 = const()[name = tensor("input_101_pad_type_0"), val = tensor("custom")]; + tensor input_101_pad_0 = const()[name = tensor("input_101_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_101_strides_0 = const()[name = tensor("input_101_strides_0"), val = tensor([1, 1])]; + tensor input_101_dilations_0 = const()[name = tensor("input_101_dilations_0"), val = tensor([1, 1])]; + tensor input_101_groups_0 = const()[name = tensor("input_101_groups_0"), val = tensor(1)]; + tensor const_39_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(422912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(570432))), name = tensor("const_39_to_fp16_palettized"), shape = tensor([128, 128, 3, 3])]; + tensor const_40_to_fp16 = const()[name = tensor("const_40_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(571008)))]; + tensor out_15_cast_fp16 = conv(bias = const_40_to_fp16, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_39_to_fp16_palettized, x = input_99_cast_fp16)[name = tensor("out_15_cast_fp16")]; + tensor input_103_pad_type_0 = const()[name = tensor("input_103_pad_type_0"), val = tensor("valid")]; + tensor input_103_strides_0 = const()[name = tensor("input_103_strides_0"), val = tensor([2, 2])]; + tensor input_103_pad_0 = const()[name = tensor("input_103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_103_dilations_0 = const()[name = tensor("input_103_dilations_0"), val = tensor([1, 1])]; + tensor input_103_groups_0 = const()[name = tensor("input_103_groups_0"), val = tensor(1)]; + tensor const_41_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(571328))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(579584))), name = tensor("const_41_to_fp16_palettized"), shape = tensor([128, 64, 1, 1])]; + tensor const_42_to_fp16 = const()[name = tensor("const_42_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(580160)))]; + tensor var_307_cast_fp16 = conv(bias = const_42_to_fp16, dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = const_41_to_fp16_palettized, x = input_93_cast_fp16)[name = tensor("op_307_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = out_15_cast_fp16, y = var_307_cast_fp16)[name = tensor("input_105_cast_fp16")]; + tensor input_107_cast_fp16 = relu(x = input_105_cast_fp16)[name = tensor("input_107_cast_fp16")]; + tensor input_109_pad_type_0 = const()[name = tensor("input_109_pad_type_0"), val = tensor("custom")]; + tensor input_109_pad_0 = const()[name = tensor("input_109_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_109_strides_0 = const()[name = tensor("input_109_strides_0"), val = tensor([1, 1])]; + tensor input_109_dilations_0 = const()[name = tensor("input_109_dilations_0"), val = tensor([1, 1])]; + tensor input_109_groups_0 = const()[name = tensor("input_109_groups_0"), val = tensor(1)]; + tensor const_43_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(580480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(728000))), name = tensor("const_43_to_fp16_palettized"), shape = tensor([128, 128, 3, 3])]; + tensor const_44_to_fp16 = const()[name = tensor("const_44_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(728576)))]; + tensor input_111_cast_fp16 = conv(bias = const_44_to_fp16, dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = const_43_to_fp16_palettized, x = input_107_cast_fp16)[name = tensor("input_111_cast_fp16")]; + tensor input_113_cast_fp16 = relu(x = input_111_cast_fp16)[name = tensor("input_113_cast_fp16")]; + tensor input_115_pad_type_0 = const()[name = tensor("input_115_pad_type_0"), val = tensor("custom")]; + tensor input_115_pad_0 = const()[name = tensor("input_115_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_115_strides_0 = const()[name = tensor("input_115_strides_0"), val = tensor([1, 1])]; + tensor input_115_dilations_0 = const()[name = tensor("input_115_dilations_0"), val = tensor([1, 1])]; + tensor input_115_groups_0 = const()[name = tensor("input_115_groups_0"), val = tensor(1)]; + tensor const_45_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(728896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(876416))), name = tensor("const_45_to_fp16_palettized"), shape = tensor([128, 128, 3, 3])]; + tensor const_46_to_fp16 = const()[name = tensor("const_46_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(876992)))]; + tensor out_17_cast_fp16 = conv(bias = const_46_to_fp16, dilations = input_115_dilations_0, groups = input_115_groups_0, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = input_115_strides_0, weight = const_45_to_fp16_palettized, x = input_113_cast_fp16)[name = tensor("out_17_cast_fp16")]; + tensor input_117_cast_fp16 = add(x = out_17_cast_fp16, y = input_107_cast_fp16)[name = tensor("input_117_cast_fp16")]; + tensor input_119_cast_fp16 = relu(x = input_117_cast_fp16)[name = tensor("input_119_cast_fp16")]; + tensor input_121_pad_type_0 = const()[name = tensor("input_121_pad_type_0"), val = tensor("custom")]; + tensor input_121_pad_0 = const()[name = tensor("input_121_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_121_strides_0 = const()[name = tensor("input_121_strides_0"), val = tensor([1, 1])]; + tensor input_121_dilations_0 = const()[name = tensor("input_121_dilations_0"), val = tensor([1, 1])]; + tensor input_121_groups_0 = const()[name = tensor("input_121_groups_0"), val = tensor(1)]; + tensor const_47_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(877312))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1024832))), name = tensor("const_47_to_fp16_palettized"), shape = tensor([128, 128, 3, 3])]; + tensor const_48_to_fp16 = const()[name = tensor("const_48_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1025408)))]; + tensor input_123_cast_fp16 = conv(bias = const_48_to_fp16, dilations = input_121_dilations_0, groups = input_121_groups_0, pad = input_121_pad_0, pad_type = input_121_pad_type_0, strides = input_121_strides_0, weight = const_47_to_fp16_palettized, x = input_119_cast_fp16)[name = tensor("input_123_cast_fp16")]; + tensor input_125_cast_fp16 = relu(x = input_123_cast_fp16)[name = tensor("input_125_cast_fp16")]; + tensor input_127_pad_type_0 = const()[name = tensor("input_127_pad_type_0"), val = tensor("custom")]; + tensor input_127_pad_0 = const()[name = tensor("input_127_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_127_strides_0 = const()[name = tensor("input_127_strides_0"), val = tensor([1, 1])]; + tensor input_127_dilations_0 = const()[name = tensor("input_127_dilations_0"), val = tensor([1, 1])]; + tensor input_127_groups_0 = const()[name = tensor("input_127_groups_0"), val = tensor(1)]; + tensor const_49_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1025728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1173248))), name = tensor("const_49_to_fp16_palettized"), shape = tensor([128, 128, 3, 3])]; + tensor const_50_to_fp16 = const()[name = tensor("const_50_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1173824)))]; + tensor out_19_cast_fp16 = conv(bias = const_50_to_fp16, dilations = input_127_dilations_0, groups = input_127_groups_0, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = input_127_strides_0, weight = const_49_to_fp16_palettized, x = input_125_cast_fp16)[name = tensor("out_19_cast_fp16")]; + tensor input_129_cast_fp16 = add(x = out_19_cast_fp16, y = input_119_cast_fp16)[name = tensor("input_129_cast_fp16")]; + tensor input_131_cast_fp16 = relu(x = input_129_cast_fp16)[name = tensor("input_131_cast_fp16")]; + tensor input_133_pad_type_0 = const()[name = tensor("input_133_pad_type_0"), val = tensor("custom")]; + tensor input_133_pad_0 = const()[name = tensor("input_133_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_133_strides_0 = const()[name = tensor("input_133_strides_0"), val = tensor([1, 1])]; + tensor input_133_dilations_0 = const()[name = tensor("input_133_dilations_0"), val = tensor([1, 1])]; + tensor input_133_groups_0 = const()[name = tensor("input_133_groups_0"), val = tensor(1)]; + tensor const_51_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1174144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1321664))), name = tensor("const_51_to_fp16_palettized"), shape = tensor([128, 128, 3, 3])]; + tensor const_52_to_fp16 = const()[name = tensor("const_52_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1322240)))]; + tensor input_135_cast_fp16 = conv(bias = const_52_to_fp16, dilations = input_133_dilations_0, groups = input_133_groups_0, pad = input_133_pad_0, pad_type = input_133_pad_type_0, strides = input_133_strides_0, weight = const_51_to_fp16_palettized, x = input_131_cast_fp16)[name = tensor("input_135_cast_fp16")]; + tensor input_137_cast_fp16 = relu(x = input_135_cast_fp16)[name = tensor("input_137_cast_fp16")]; + tensor input_139_pad_type_0 = const()[name = tensor("input_139_pad_type_0"), val = tensor("custom")]; + tensor input_139_pad_0 = const()[name = tensor("input_139_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_139_strides_0 = const()[name = tensor("input_139_strides_0"), val = tensor([1, 1])]; + tensor input_139_dilations_0 = const()[name = tensor("input_139_dilations_0"), val = tensor([1, 1])]; + tensor input_139_groups_0 = const()[name = tensor("input_139_groups_0"), val = tensor(1)]; + tensor const_53_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1322560))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1470080))), name = tensor("const_53_to_fp16_palettized"), shape = tensor([128, 128, 3, 3])]; + tensor const_54_to_fp16 = const()[name = tensor("const_54_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1470656)))]; + tensor out_21_cast_fp16 = conv(bias = const_54_to_fp16, dilations = input_139_dilations_0, groups = input_139_groups_0, pad = input_139_pad_0, pad_type = input_139_pad_type_0, strides = input_139_strides_0, weight = const_53_to_fp16_palettized, x = input_137_cast_fp16)[name = tensor("out_21_cast_fp16")]; + tensor input_141_cast_fp16 = add(x = out_21_cast_fp16, y = input_131_cast_fp16)[name = tensor("input_141_cast_fp16")]; + tensor input_143_cast_fp16 = relu(x = input_141_cast_fp16)[name = tensor("input_143_cast_fp16")]; + tensor input_145_pad_type_0 = const()[name = tensor("input_145_pad_type_0"), val = tensor("custom")]; + tensor input_145_pad_0 = const()[name = tensor("input_145_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_145_strides_0 = const()[name = tensor("input_145_strides_0"), val = tensor([1, 1])]; + tensor input_145_dilations_0 = const()[name = tensor("input_145_dilations_0"), val = tensor([1, 1])]; + tensor input_145_groups_0 = const()[name = tensor("input_145_groups_0"), val = tensor(1)]; + tensor const_55_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1470976))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1618496))), name = tensor("const_55_to_fp16_palettized"), shape = tensor([128, 128, 3, 3])]; + tensor const_56_to_fp16 = const()[name = tensor("const_56_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1619072)))]; + tensor input_147_cast_fp16 = conv(bias = const_56_to_fp16, dilations = input_145_dilations_0, groups = input_145_groups_0, pad = input_145_pad_0, pad_type = input_145_pad_type_0, strides = input_145_strides_0, weight = const_55_to_fp16_palettized, x = input_143_cast_fp16)[name = tensor("input_147_cast_fp16")]; + tensor input_149_cast_fp16 = relu(x = input_147_cast_fp16)[name = tensor("input_149_cast_fp16")]; + tensor input_151_pad_type_0 = const()[name = tensor("input_151_pad_type_0"), val = tensor("custom")]; + tensor input_151_pad_0 = const()[name = tensor("input_151_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_151_strides_0 = const()[name = tensor("input_151_strides_0"), val = tensor([1, 1])]; + tensor input_151_dilations_0 = const()[name = tensor("input_151_dilations_0"), val = tensor([1, 1])]; + tensor input_151_groups_0 = const()[name = tensor("input_151_groups_0"), val = tensor(1)]; + tensor const_57_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1619392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1766912))), name = tensor("const_57_to_fp16_palettized"), shape = tensor([128, 128, 3, 3])]; + tensor const_58_to_fp16 = const()[name = tensor("const_58_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1767488)))]; + tensor out_23_cast_fp16 = conv(bias = const_58_to_fp16, dilations = input_151_dilations_0, groups = input_151_groups_0, pad = input_151_pad_0, pad_type = input_151_pad_type_0, strides = input_151_strides_0, weight = const_57_to_fp16_palettized, x = input_149_cast_fp16)[name = tensor("out_23_cast_fp16")]; + tensor input_153_cast_fp16 = add(x = out_23_cast_fp16, y = input_143_cast_fp16)[name = tensor("input_153_cast_fp16")]; + tensor input_155_cast_fp16 = relu(x = input_153_cast_fp16)[name = tensor("input_155_cast_fp16")]; + tensor input_157_pad_type_0 = const()[name = tensor("input_157_pad_type_0"), val = tensor("custom")]; + tensor input_157_pad_0 = const()[name = tensor("input_157_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_157_strides_0 = const()[name = tensor("input_157_strides_0"), val = tensor([1, 1])]; + tensor input_157_dilations_0 = const()[name = tensor("input_157_dilations_0"), val = tensor([1, 1])]; + tensor input_157_groups_0 = const()[name = tensor("input_157_groups_0"), val = tensor(1)]; + tensor const_59_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1767808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1915328))), name = tensor("const_59_to_fp16_palettized"), shape = tensor([128, 128, 3, 3])]; + tensor const_60_to_fp16 = const()[name = tensor("const_60_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1915904)))]; + tensor input_159_cast_fp16 = conv(bias = const_60_to_fp16, dilations = input_157_dilations_0, groups = input_157_groups_0, pad = input_157_pad_0, pad_type = input_157_pad_type_0, strides = input_157_strides_0, weight = const_59_to_fp16_palettized, x = input_155_cast_fp16)[name = tensor("input_159_cast_fp16")]; + tensor input_161_cast_fp16 = relu(x = input_159_cast_fp16)[name = tensor("input_161_cast_fp16")]; + tensor input_163_pad_type_0 = const()[name = tensor("input_163_pad_type_0"), val = tensor("custom")]; + tensor input_163_pad_0 = const()[name = tensor("input_163_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_163_strides_0 = const()[name = tensor("input_163_strides_0"), val = tensor([1, 1])]; + tensor input_163_dilations_0 = const()[name = tensor("input_163_dilations_0"), val = tensor([1, 1])]; + tensor input_163_groups_0 = const()[name = tensor("input_163_groups_0"), val = tensor(1)]; + tensor const_61_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1916224))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2063744))), name = tensor("const_61_to_fp16_palettized"), shape = tensor([128, 128, 3, 3])]; + tensor const_62_to_fp16 = const()[name = tensor("const_62_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2064320)))]; + tensor out_25_cast_fp16 = conv(bias = const_62_to_fp16, dilations = input_163_dilations_0, groups = input_163_groups_0, pad = input_163_pad_0, pad_type = input_163_pad_type_0, strides = input_163_strides_0, weight = const_61_to_fp16_palettized, x = input_161_cast_fp16)[name = tensor("out_25_cast_fp16")]; + tensor input_165_cast_fp16 = add(x = out_25_cast_fp16, y = input_155_cast_fp16)[name = tensor("input_165_cast_fp16")]; + tensor input_167_cast_fp16 = relu(x = input_165_cast_fp16)[name = tensor("input_167_cast_fp16")]; + tensor input_169_pad_type_0 = const()[name = tensor("input_169_pad_type_0"), val = tensor("custom")]; + tensor input_169_pad_0 = const()[name = tensor("input_169_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_169_strides_0 = const()[name = tensor("input_169_strides_0"), val = tensor([2, 2])]; + tensor input_169_dilations_0 = const()[name = tensor("input_169_dilations_0"), val = tensor([1, 1])]; + tensor input_169_groups_0 = const()[name = tensor("input_169_groups_0"), val = tensor(1)]; + tensor const_63_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2064640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2359616))), name = tensor("const_63_to_fp16_palettized"), shape = tensor([256, 128, 3, 3])]; + tensor const_64_to_fp16 = const()[name = tensor("const_64_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2360192)))]; + tensor input_171_cast_fp16 = conv(bias = const_64_to_fp16, dilations = input_169_dilations_0, groups = input_169_groups_0, pad = input_169_pad_0, pad_type = input_169_pad_type_0, strides = input_169_strides_0, weight = const_63_to_fp16_palettized, x = input_167_cast_fp16)[name = tensor("input_171_cast_fp16")]; + tensor input_173_cast_fp16 = relu(x = input_171_cast_fp16)[name = tensor("input_173_cast_fp16")]; + tensor input_175_pad_type_0 = const()[name = tensor("input_175_pad_type_0"), val = tensor("custom")]; + tensor input_175_pad_0 = const()[name = tensor("input_175_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_175_strides_0 = const()[name = tensor("input_175_strides_0"), val = tensor([1, 1])]; + tensor input_175_dilations_0 = const()[name = tensor("input_175_dilations_0"), val = tensor([1, 1])]; + tensor input_175_groups_0 = const()[name = tensor("input_175_groups_0"), val = tensor(1)]; + tensor const_65_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2360768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2950656))), name = tensor("const_65_to_fp16_palettized"), shape = tensor([256, 256, 3, 3])]; + tensor const_66_to_fp16 = const()[name = tensor("const_66_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2951232)))]; + tensor out_27_cast_fp16 = conv(bias = const_66_to_fp16, dilations = input_175_dilations_0, groups = input_175_groups_0, pad = input_175_pad_0, pad_type = input_175_pad_type_0, strides = input_175_strides_0, weight = const_65_to_fp16_palettized, x = input_173_cast_fp16)[name = tensor("out_27_cast_fp16")]; + tensor input_177_pad_type_0 = const()[name = tensor("input_177_pad_type_0"), val = tensor("valid")]; + tensor input_177_strides_0 = const()[name = tensor("input_177_strides_0"), val = tensor([2, 2])]; + tensor input_177_pad_0 = const()[name = tensor("input_177_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_177_dilations_0 = const()[name = tensor("input_177_dilations_0"), val = tensor([1, 1])]; + tensor input_177_groups_0 = const()[name = tensor("input_177_groups_0"), val = tensor(1)]; + tensor const_67_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2951808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2984640))), name = tensor("const_67_to_fp16_palettized"), shape = tensor([256, 128, 1, 1])]; + tensor const_68_to_fp16 = const()[name = tensor("const_68_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2985216)))]; + tensor var_498_cast_fp16 = conv(bias = const_68_to_fp16, dilations = input_177_dilations_0, groups = input_177_groups_0, pad = input_177_pad_0, pad_type = input_177_pad_type_0, strides = input_177_strides_0, weight = const_67_to_fp16_palettized, x = input_167_cast_fp16)[name = tensor("op_498_cast_fp16")]; + tensor input_179_cast_fp16 = add(x = out_27_cast_fp16, y = var_498_cast_fp16)[name = tensor("input_179_cast_fp16")]; + tensor input_181_cast_fp16 = relu(x = input_179_cast_fp16)[name = tensor("input_181_cast_fp16")]; + tensor input_183_pad_type_0 = const()[name = tensor("input_183_pad_type_0"), val = tensor("custom")]; + tensor input_183_pad_0 = const()[name = tensor("input_183_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_183_strides_0 = const()[name = tensor("input_183_strides_0"), val = tensor([1, 1])]; + tensor input_183_dilations_0 = const()[name = tensor("input_183_dilations_0"), val = tensor([1, 1])]; + tensor input_183_groups_0 = const()[name = tensor("input_183_groups_0"), val = tensor(1)]; + tensor const_69_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2985792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3575680))), name = tensor("const_69_to_fp16_palettized"), shape = tensor([256, 256, 3, 3])]; + tensor const_70_to_fp16 = const()[name = tensor("const_70_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3576256)))]; + tensor input_185_cast_fp16 = conv(bias = const_70_to_fp16, dilations = input_183_dilations_0, groups = input_183_groups_0, pad = input_183_pad_0, pad_type = input_183_pad_type_0, strides = input_183_strides_0, weight = const_69_to_fp16_palettized, x = input_181_cast_fp16)[name = tensor("input_185_cast_fp16")]; + tensor input_187_cast_fp16 = relu(x = input_185_cast_fp16)[name = tensor("input_187_cast_fp16")]; + tensor input_189_pad_type_0 = const()[name = tensor("input_189_pad_type_0"), val = tensor("custom")]; + tensor input_189_pad_0 = const()[name = tensor("input_189_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_189_strides_0 = const()[name = tensor("input_189_strides_0"), val = tensor([1, 1])]; + tensor input_189_dilations_0 = const()[name = tensor("input_189_dilations_0"), val = tensor([1, 1])]; + tensor input_189_groups_0 = const()[name = tensor("input_189_groups_0"), val = tensor(1)]; + tensor const_71_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3576832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4166720))), name = tensor("const_71_to_fp16_palettized"), shape = tensor([256, 256, 3, 3])]; + tensor const_72_to_fp16 = const()[name = tensor("const_72_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4167296)))]; + tensor out_29_cast_fp16 = conv(bias = const_72_to_fp16, dilations = input_189_dilations_0, groups = input_189_groups_0, pad = input_189_pad_0, pad_type = input_189_pad_type_0, strides = input_189_strides_0, weight = const_71_to_fp16_palettized, x = input_187_cast_fp16)[name = tensor("out_29_cast_fp16")]; + tensor input_191_cast_fp16 = add(x = out_29_cast_fp16, y = input_181_cast_fp16)[name = tensor("input_191_cast_fp16")]; + tensor input_193_cast_fp16 = relu(x = input_191_cast_fp16)[name = tensor("input_193_cast_fp16")]; + tensor input_195_pad_type_0 = const()[name = tensor("input_195_pad_type_0"), val = tensor("custom")]; + tensor input_195_pad_0 = const()[name = tensor("input_195_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_195_strides_0 = const()[name = tensor("input_195_strides_0"), val = tensor([1, 1])]; + tensor input_195_dilations_0 = const()[name = tensor("input_195_dilations_0"), val = tensor([1, 1])]; + tensor input_195_groups_0 = const()[name = tensor("input_195_groups_0"), val = tensor(1)]; + tensor const_73_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4167872))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4757760))), name = tensor("const_73_to_fp16_palettized"), shape = tensor([256, 256, 3, 3])]; + tensor const_74_to_fp16 = const()[name = tensor("const_74_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4758336)))]; + tensor input_197_cast_fp16 = conv(bias = const_74_to_fp16, dilations = input_195_dilations_0, groups = input_195_groups_0, pad = input_195_pad_0, pad_type = input_195_pad_type_0, strides = input_195_strides_0, weight = const_73_to_fp16_palettized, x = input_193_cast_fp16)[name = tensor("input_197_cast_fp16")]; + tensor input_199_cast_fp16 = relu(x = input_197_cast_fp16)[name = tensor("input_199_cast_fp16")]; + tensor input_201_pad_type_0 = const()[name = tensor("input_201_pad_type_0"), val = tensor("custom")]; + tensor input_201_pad_0 = const()[name = tensor("input_201_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_201_strides_0 = const()[name = tensor("input_201_strides_0"), val = tensor([1, 1])]; + tensor input_201_dilations_0 = const()[name = tensor("input_201_dilations_0"), val = tensor([1, 1])]; + tensor input_201_groups_0 = const()[name = tensor("input_201_groups_0"), val = tensor(1)]; + tensor const_75_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4758912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5348800))), name = tensor("const_75_to_fp16_palettized"), shape = tensor([256, 256, 3, 3])]; + tensor const_76_to_fp16 = const()[name = tensor("const_76_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5349376)))]; + tensor out_cast_fp16 = conv(bias = const_76_to_fp16, dilations = input_201_dilations_0, groups = input_201_groups_0, pad = input_201_pad_0, pad_type = input_201_pad_type_0, strides = input_201_strides_0, weight = const_75_to_fp16_palettized, x = input_199_cast_fp16)[name = tensor("out_cast_fp16")]; + tensor input_203_cast_fp16 = add(x = out_cast_fp16, y = input_193_cast_fp16)[name = tensor("input_203_cast_fp16")]; + tensor x_cast_fp16 = relu(x = input_203_cast_fp16)[name = tensor("x_cast_fp16")]; + tensor var_577 = const()[name = tensor("op_577"), val = tensor([1, 2560, 125])]; + tensor sequences_cast_fp16 = reshape(shape = var_577, x = x_cast_fp16)[name = tensor("sequences_cast_fp16")]; + tensor expand_dims_0_axes_0 = const()[name = tensor("expand_dims_0_axes_0"), val = tensor([3])]; + tensor expand_dims_0_cast_fp16 = expand_dims(axes = expand_dims_0_axes_0, x = speaker_masks)[name = tensor("expand_dims_0_cast_fp16")]; + tensor upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = tensor("upsample_nearest_neighbor_0_scale_factor_height_0"), val = tensor(0x1.b2a2a4p-3)]; + tensor upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = tensor("upsample_nearest_neighbor_0_scale_factor_width_0"), val = tensor(0x1p+0)]; + tensor upsample_nearest_neighbor_0_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0_cast_fp16)[name = tensor("upsample_nearest_neighbor_0_cast_fp16")]; + tensor weights_1_axes_0 = const()[name = tensor("weights_1_axes_0"), val = tensor([3])]; + tensor weights_1_cast_fp16 = squeeze(axes = weights_1_axes_0, x = upsample_nearest_neighbor_0_cast_fp16)[name = tensor("weights_1_cast_fp16")]; + tensor var_583_begin_0 = const()[name = tensor("op_583_begin_0"), val = tensor([0, 0, 0])]; + tensor var_583_end_0 = const()[name = tensor("op_583_end_0"), val = tensor([1, 1, 125])]; + tensor var_583_end_mask_0 = const()[name = tensor("op_583_end_mask_0"), val = tensor([true, false, true])]; + tensor var_583_squeeze_mask_0 = const()[name = tensor("op_583_squeeze_mask_0"), val = tensor([false, true, false])]; + tensor var_583_cast_fp16 = slice_by_index(begin = var_583_begin_0, end = var_583_end_0, end_mask = var_583_end_mask_0, squeeze_mask = var_583_squeeze_mask_0, x = weights_1_cast_fp16)[name = tensor("op_583_cast_fp16")]; + tensor weights_5_axes_0 = const()[name = tensor("weights_5_axes_0"), val = tensor([1])]; + tensor weights_5_cast_fp16 = expand_dims(axes = weights_5_axes_0, x = var_583_cast_fp16)[name = tensor("weights_5_cast_fp16")]; + tensor var_587_axes_0 = const()[name = tensor("op_587_axes_0"), val = tensor([2])]; + tensor var_587_keep_dims_0 = const()[name = tensor("op_587_keep_dims_0"), val = tensor(false)]; + tensor var_587_cast_fp16 = reduce_sum(axes = var_587_axes_0, keep_dims = var_587_keep_dims_0, x = weights_5_cast_fp16)[name = tensor("op_587_cast_fp16")]; + tensor var_588_to_fp16 = const()[name = tensor("op_588_to_fp16"), val = tensor(0x1p-24)]; + tensor v1_1_cast_fp16 = add(x = var_587_cast_fp16, y = var_588_to_fp16)[name = tensor("v1_1_cast_fp16")]; + tensor var_590_cast_fp16 = mul(x = sequences_cast_fp16, y = weights_5_cast_fp16)[name = tensor("op_590_cast_fp16")]; + tensor var_592_axes_0 = const()[name = tensor("op_592_axes_0"), val = tensor([2])]; + tensor var_592_keep_dims_0 = const()[name = tensor("op_592_keep_dims_0"), val = tensor(false)]; + tensor var_592_cast_fp16 = reduce_sum(axes = var_592_axes_0, keep_dims = var_592_keep_dims_0, x = var_590_cast_fp16)[name = tensor("op_592_cast_fp16")]; + tensor mean_1_cast_fp16 = real_div(x = var_592_cast_fp16, y = v1_1_cast_fp16)[name = tensor("mean_1_cast_fp16")]; + tensor var_594_axes_0 = const()[name = tensor("op_594_axes_0"), val = tensor([2])]; + tensor var_594_cast_fp16 = expand_dims(axes = var_594_axes_0, x = mean_1_cast_fp16)[name = tensor("op_594_cast_fp16")]; + tensor var_595_cast_fp16 = sub(x = sequences_cast_fp16, y = var_594_cast_fp16)[name = tensor("op_595_cast_fp16")]; + tensor dx2_1_cast_fp16 = mul(x = var_595_cast_fp16, y = var_595_cast_fp16)[name = tensor("dx2_1_cast_fp16")]; + tensor var_597_cast_fp16 = mul(x = weights_5_cast_fp16, y = weights_5_cast_fp16)[name = tensor("op_597_cast_fp16")]; + tensor v2_1_axes_0 = const()[name = tensor("v2_1_axes_0"), val = tensor([2])]; + tensor v2_1_keep_dims_0 = const()[name = tensor("v2_1_keep_dims_0"), val = tensor(false)]; + tensor v2_1_cast_fp16 = reduce_sum(axes = v2_1_axes_0, keep_dims = v2_1_keep_dims_0, x = var_597_cast_fp16)[name = tensor("v2_1_cast_fp16")]; + tensor var_600_cast_fp16 = mul(x = dx2_1_cast_fp16, y = weights_5_cast_fp16)[name = tensor("op_600_cast_fp16")]; + tensor var_602_axes_0 = const()[name = tensor("op_602_axes_0"), val = tensor([2])]; + tensor var_602_keep_dims_0 = const()[name = tensor("op_602_keep_dims_0"), val = tensor(false)]; + tensor var_602_cast_fp16 = reduce_sum(axes = var_602_axes_0, keep_dims = var_602_keep_dims_0, x = var_600_cast_fp16)[name = tensor("op_602_cast_fp16")]; + tensor var_603_cast_fp16 = real_div(x = v2_1_cast_fp16, y = v1_1_cast_fp16)[name = tensor("op_603_cast_fp16")]; + tensor var_604_cast_fp16 = sub(x = v1_1_cast_fp16, y = var_603_cast_fp16)[name = tensor("op_604_cast_fp16")]; + tensor var_605_to_fp16 = const()[name = tensor("op_605_to_fp16"), val = tensor(0x1p-24)]; + tensor var_606_cast_fp16 = add(x = var_604_cast_fp16, y = var_605_to_fp16)[name = tensor("op_606_cast_fp16")]; + tensor var_1_cast_fp16 = real_div(x = var_602_cast_fp16, y = var_606_cast_fp16)[name = tensor("var_1_cast_fp16")]; + tensor std_1_cast_fp16 = sqrt(x = var_1_cast_fp16)[name = tensor("std_1_cast_fp16")]; + tensor var_610_interleave_0 = const()[name = tensor("op_610_interleave_0"), val = tensor(false)]; + tensor var_610_cast_fp16 = concat(axis = var_12, interleave = var_610_interleave_0, values = (mean_1_cast_fp16, std_1_cast_fp16))[name = tensor("op_610_cast_fp16")]; + tensor var_612_begin_0 = const()[name = tensor("op_612_begin_0"), val = tensor([0, 1, 0])]; + tensor var_612_end_0 = const()[name = tensor("op_612_end_0"), val = tensor([1, 2, 125])]; + tensor var_612_end_mask_0 = const()[name = tensor("op_612_end_mask_0"), val = tensor([true, false, true])]; + tensor var_612_squeeze_mask_0 = const()[name = tensor("op_612_squeeze_mask_0"), val = tensor([false, true, false])]; + tensor var_612_cast_fp16 = slice_by_index(begin = var_612_begin_0, end = var_612_end_0, end_mask = var_612_end_mask_0, squeeze_mask = var_612_squeeze_mask_0, x = weights_1_cast_fp16)[name = tensor("op_612_cast_fp16")]; + tensor weights_9_axes_0 = const()[name = tensor("weights_9_axes_0"), val = tensor([1])]; + tensor weights_9_cast_fp16 = expand_dims(axes = weights_9_axes_0, x = var_612_cast_fp16)[name = tensor("weights_9_cast_fp16")]; + tensor var_616_axes_0 = const()[name = tensor("op_616_axes_0"), val = tensor([2])]; + tensor var_616_keep_dims_0 = const()[name = tensor("op_616_keep_dims_0"), val = tensor(false)]; + tensor var_616_cast_fp16 = reduce_sum(axes = var_616_axes_0, keep_dims = var_616_keep_dims_0, x = weights_9_cast_fp16)[name = tensor("op_616_cast_fp16")]; + tensor var_617_to_fp16 = const()[name = tensor("op_617_to_fp16"), val = tensor(0x1p-24)]; + tensor v1_3_cast_fp16 = add(x = var_616_cast_fp16, y = var_617_to_fp16)[name = tensor("v1_3_cast_fp16")]; + tensor var_619_cast_fp16 = mul(x = sequences_cast_fp16, y = weights_9_cast_fp16)[name = tensor("op_619_cast_fp16")]; + tensor var_621_axes_0 = const()[name = tensor("op_621_axes_0"), val = tensor([2])]; + tensor var_621_keep_dims_0 = const()[name = tensor("op_621_keep_dims_0"), val = tensor(false)]; + tensor var_621_cast_fp16 = reduce_sum(axes = var_621_axes_0, keep_dims = var_621_keep_dims_0, x = var_619_cast_fp16)[name = tensor("op_621_cast_fp16")]; + tensor mean_3_cast_fp16 = real_div(x = var_621_cast_fp16, y = v1_3_cast_fp16)[name = tensor("mean_3_cast_fp16")]; + tensor var_623_axes_0 = const()[name = tensor("op_623_axes_0"), val = tensor([2])]; + tensor var_623_cast_fp16 = expand_dims(axes = var_623_axes_0, x = mean_3_cast_fp16)[name = tensor("op_623_cast_fp16")]; + tensor var_624_cast_fp16 = sub(x = sequences_cast_fp16, y = var_623_cast_fp16)[name = tensor("op_624_cast_fp16")]; + tensor dx2_3_cast_fp16 = mul(x = var_624_cast_fp16, y = var_624_cast_fp16)[name = tensor("dx2_3_cast_fp16")]; + tensor var_626_cast_fp16 = mul(x = weights_9_cast_fp16, y = weights_9_cast_fp16)[name = tensor("op_626_cast_fp16")]; + tensor v2_3_axes_0 = const()[name = tensor("v2_3_axes_0"), val = tensor([2])]; + tensor v2_3_keep_dims_0 = const()[name = tensor("v2_3_keep_dims_0"), val = tensor(false)]; + tensor v2_3_cast_fp16 = reduce_sum(axes = v2_3_axes_0, keep_dims = v2_3_keep_dims_0, x = var_626_cast_fp16)[name = tensor("v2_3_cast_fp16")]; + tensor var_629_cast_fp16 = mul(x = dx2_3_cast_fp16, y = weights_9_cast_fp16)[name = tensor("op_629_cast_fp16")]; + tensor var_631_axes_0 = const()[name = tensor("op_631_axes_0"), val = tensor([2])]; + tensor var_631_keep_dims_0 = const()[name = tensor("op_631_keep_dims_0"), val = tensor(false)]; + tensor var_631_cast_fp16 = reduce_sum(axes = var_631_axes_0, keep_dims = var_631_keep_dims_0, x = var_629_cast_fp16)[name = tensor("op_631_cast_fp16")]; + tensor var_632_cast_fp16 = real_div(x = v2_3_cast_fp16, y = v1_3_cast_fp16)[name = tensor("op_632_cast_fp16")]; + tensor var_633_cast_fp16 = sub(x = v1_3_cast_fp16, y = var_632_cast_fp16)[name = tensor("op_633_cast_fp16")]; + tensor var_634_to_fp16 = const()[name = tensor("op_634_to_fp16"), val = tensor(0x1p-24)]; + tensor var_635_cast_fp16 = add(x = var_633_cast_fp16, y = var_634_to_fp16)[name = tensor("op_635_cast_fp16")]; + tensor var_3_cast_fp16 = real_div(x = var_631_cast_fp16, y = var_635_cast_fp16)[name = tensor("var_3_cast_fp16")]; + tensor std_3_cast_fp16 = sqrt(x = var_3_cast_fp16)[name = tensor("std_3_cast_fp16")]; + tensor var_639_interleave_0 = const()[name = tensor("op_639_interleave_0"), val = tensor(false)]; + tensor var_639_cast_fp16 = concat(axis = var_12, interleave = var_639_interleave_0, values = (mean_3_cast_fp16, std_3_cast_fp16))[name = tensor("op_639_cast_fp16")]; + tensor var_641_begin_0 = const()[name = tensor("op_641_begin_0"), val = tensor([0, 2, 0])]; + tensor var_641_end_0 = const()[name = tensor("op_641_end_0"), val = tensor([1, 3, 125])]; + tensor var_641_end_mask_0 = const()[name = tensor("op_641_end_mask_0"), val = tensor([true, false, true])]; + tensor var_641_squeeze_mask_0 = const()[name = tensor("op_641_squeeze_mask_0"), val = tensor([false, true, false])]; + tensor var_641_cast_fp16 = slice_by_index(begin = var_641_begin_0, end = var_641_end_0, end_mask = var_641_end_mask_0, squeeze_mask = var_641_squeeze_mask_0, x = weights_1_cast_fp16)[name = tensor("op_641_cast_fp16")]; + tensor weights_axes_0 = const()[name = tensor("weights_axes_0"), val = tensor([1])]; + tensor weights_cast_fp16 = expand_dims(axes = weights_axes_0, x = var_641_cast_fp16)[name = tensor("weights_cast_fp16")]; + tensor var_645_axes_0 = const()[name = tensor("op_645_axes_0"), val = tensor([2])]; + tensor var_645_keep_dims_0 = const()[name = tensor("op_645_keep_dims_0"), val = tensor(false)]; + tensor var_645_cast_fp16 = reduce_sum(axes = var_645_axes_0, keep_dims = var_645_keep_dims_0, x = weights_cast_fp16)[name = tensor("op_645_cast_fp16")]; + tensor var_646_to_fp16 = const()[name = tensor("op_646_to_fp16"), val = tensor(0x1p-24)]; + tensor v1_cast_fp16 = add(x = var_645_cast_fp16, y = var_646_to_fp16)[name = tensor("v1_cast_fp16")]; + tensor var_648_cast_fp16 = mul(x = sequences_cast_fp16, y = weights_cast_fp16)[name = tensor("op_648_cast_fp16")]; + tensor var_650_axes_0 = const()[name = tensor("op_650_axes_0"), val = tensor([2])]; + tensor var_650_keep_dims_0 = const()[name = tensor("op_650_keep_dims_0"), val = tensor(false)]; + tensor var_650_cast_fp16 = reduce_sum(axes = var_650_axes_0, keep_dims = var_650_keep_dims_0, x = var_648_cast_fp16)[name = tensor("op_650_cast_fp16")]; + tensor mean_cast_fp16 = real_div(x = var_650_cast_fp16, y = v1_cast_fp16)[name = tensor("mean_cast_fp16")]; + tensor var_652_axes_0 = const()[name = tensor("op_652_axes_0"), val = tensor([2])]; + tensor var_652_cast_fp16 = expand_dims(axes = var_652_axes_0, x = mean_cast_fp16)[name = tensor("op_652_cast_fp16")]; + tensor var_653_cast_fp16 = sub(x = sequences_cast_fp16, y = var_652_cast_fp16)[name = tensor("op_653_cast_fp16")]; + tensor dx2_cast_fp16 = mul(x = var_653_cast_fp16, y = var_653_cast_fp16)[name = tensor("dx2_cast_fp16")]; + tensor var_655_cast_fp16 = mul(x = weights_cast_fp16, y = weights_cast_fp16)[name = tensor("op_655_cast_fp16")]; + tensor v2_axes_0 = const()[name = tensor("v2_axes_0"), val = tensor([2])]; + tensor v2_keep_dims_0 = const()[name = tensor("v2_keep_dims_0"), val = tensor(false)]; + tensor v2_cast_fp16 = reduce_sum(axes = v2_axes_0, keep_dims = v2_keep_dims_0, x = var_655_cast_fp16)[name = tensor("v2_cast_fp16")]; + tensor var_658_cast_fp16 = mul(x = dx2_cast_fp16, y = weights_cast_fp16)[name = tensor("op_658_cast_fp16")]; + tensor var_660_axes_0 = const()[name = tensor("op_660_axes_0"), val = tensor([2])]; + tensor var_660_keep_dims_0 = const()[name = tensor("op_660_keep_dims_0"), val = tensor(false)]; + tensor var_660_cast_fp16 = reduce_sum(axes = var_660_axes_0, keep_dims = var_660_keep_dims_0, x = var_658_cast_fp16)[name = tensor("op_660_cast_fp16")]; + tensor var_661_cast_fp16 = real_div(x = v2_cast_fp16, y = v1_cast_fp16)[name = tensor("op_661_cast_fp16")]; + tensor var_662_cast_fp16 = sub(x = v1_cast_fp16, y = var_661_cast_fp16)[name = tensor("op_662_cast_fp16")]; + tensor var_663_to_fp16 = const()[name = tensor("op_663_to_fp16"), val = tensor(0x1p-24)]; + tensor var_664_cast_fp16 = add(x = var_662_cast_fp16, y = var_663_to_fp16)[name = tensor("op_664_cast_fp16")]; + tensor var_cast_fp16 = real_div(x = var_660_cast_fp16, y = var_664_cast_fp16)[name = tensor("var_cast_fp16")]; + tensor std_cast_fp16 = sqrt(x = var_cast_fp16)[name = tensor("std_cast_fp16")]; + tensor var_668_interleave_0 = const()[name = tensor("op_668_interleave_0"), val = tensor(false)]; + tensor var_668_cast_fp16 = concat(axis = var_12, interleave = var_668_interleave_0, values = (mean_cast_fp16, std_cast_fp16))[name = tensor("op_668_cast_fp16")]; + tensor input_axis_0 = const()[name = tensor("input_axis_0"), val = tensor(1)]; + tensor input_cast_fp16 = stack(axis = input_axis_0, values = (var_610_cast_fp16, var_639_cast_fp16, var_668_cast_fp16))[name = tensor("input_cast_fp16")]; + tensor model_resnet_seg_1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5349952))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6660736))), name = tensor("model_resnet_seg_1_weight_to_fp16_palettized"), shape = tensor([256, 5120])]; + tensor model_resnet_seg_1_bias_to_fp16 = const()[name = tensor("model_resnet_seg_1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6661312)))]; + tensor speaker_embeddings = linear(bias = model_resnet_seg_1_bias_to_fp16, weight = model_resnet_seg_1_weight_to_fp16_palettized, x = input_cast_fp16)[name = tensor("linear_0_cast_fp16")]; + } -> (speaker_embeddings); +} \ No newline at end of file diff --git a/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedder.mlmodelc/weights/weight.bin b/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedder.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..6c1f0b14a1a8ca16cf05adcda4d6f1abffb052a0 --- /dev/null +++ b/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedder.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e733a81059912ad73b02ce82ae271b40e0767ac6b155dfca84a6c3a0d753d02f +size 6661888 diff --git a/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedderPreprocessor.mlmodelc/analytics/coremldata.bin b/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedderPreprocessor.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..520300aa0cb8e55941dca9a5ec6b0f9e5fd29e0c --- /dev/null +++ b/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedderPreprocessor.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5dd1e6ea694479da669d42d9752db8ebffdc7582b80c90f06452e2ed1f72cf8f +size 243 diff --git a/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedderPreprocessor.mlmodelc/coremldata.bin b/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedderPreprocessor.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..1d4de2f7d93de6f55ff3271345ba9956d8c72853 --- /dev/null +++ b/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedderPreprocessor.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f252f1834b495a132333af500f573a7218c2d3d1f7bfb0faaad89c51a989dac7 +size 330 diff --git a/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedderPreprocessor.mlmodelc/metadata.json b/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedderPreprocessor.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..56653308579c1c17b64994231aa3002f33916398 --- /dev/null +++ b/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedderPreprocessor.mlmodelc/metadata.json @@ -0,0 +1,77 @@ +[ + { + "metadataOutputVersion" : "3.0", + "storagePrecision" : "Float32", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 1 × 998 × 80)", + "shortDescription" : "", + "shape" : "[1, 998, 80]", + "name" : "preprocessor_output_1", + "type" : "MultiArray" + } + ], + "modelParameters" : [ + + ], + "specificationVersion" : 7, + "mlProgramOperationTypeHistogram" : { + "Ios16.cast" : 2, + "Ios16.mul" : 4, + "SliceByIndex" : 2, + "Transpose" : 2, + "SlidingWindows" : 1, + "Ios16.sub" : 3, + "Ios16.log" : 1, + "Ios16.reduceMean" : 2, + "Ios16.square" : 2, + "Squeeze" : 2, + "Ios16.matmul" : 2, + "Ios16.add" : 1, + "Ios16.linear" : 1, + "ExpandDims" : 4, + "Ios16.gather" : 2, + "Ios16.maximum" : 1, + "Identity" : 1, + "Pad" : 2 + }, + "computePrecision" : "Mixed (Float16, Float32, Int32)", + "isUpdatable" : "0", + "stateSchema" : [ + + ], + "availability" : { + "macOS" : "13.0", + "tvOS" : "16.0", + "visionOS" : "1.0", + "watchOS" : "9.0", + "iOS" : "16.0", + "macCatalyst" : "16.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "userDefinedMetadata" : { + "com.github.apple.coremltools.source_dialect" : "TorchScript", + "com.github.apple.coremltools.version" : "8.1", + "com.github.apple.coremltools.source" : "torch==2.5.1" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 1 × 160000)", + "shortDescription" : "", + "shape" : "[1, 160000]", + "name" : "waveforms", + "type" : "MultiArray" + } + ], + "generatedClassName" : "SpeakerEmbeddingPreprocessor", + "method" : "predict" + } +] \ No newline at end of file diff --git a/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedderPreprocessor.mlmodelc/model.mil b/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedderPreprocessor.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..3bdac730dbc6970716314c338a4e209fa1f86f21 --- /dev/null +++ b/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedderPreprocessor.mlmodelc/model.mil @@ -0,0 +1,90 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3401.3.1"}, {"coremlc-version", "3401.4.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.1"}})] +{ + func main(tensor waveforms) { + tensor cast_0_dtype_0 = const()[name = tensor("cast_0_dtype_0"), val = tensor("fp32")]; + tensor var_2_promoted = const()[name = tensor("op_2_promoted"), val = tensor(0x1p+15)]; + tensor cast_0 = cast(dtype = cast_0_dtype_0, x = waveforms)[name = tensor("cast_11")]; + tensor waveform_1 = mul(x = cast_0, y = var_2_promoted)[name = tensor("waveform_1")]; + tensor var_6_begin_0 = const()[name = tensor("op_6_begin_0"), val = tensor([0, 0])]; + tensor var_6_end_0 = const()[name = tensor("op_6_end_0"), val = tensor([1, 160000])]; + tensor var_6_end_mask_0 = const()[name = tensor("op_6_end_mask_0"), val = tensor([false, true])]; + tensor var_6_squeeze_mask_0 = const()[name = tensor("op_6_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_6 = slice_by_index(begin = var_6_begin_0, end = var_6_end_0, end_mask = var_6_end_mask_0, squeeze_mask = var_6_squeeze_mask_0, x = waveform_1)[name = tensor("op_6")]; + tensor sliding_windows_0_axis_0 = const()[name = tensor("sliding_windows_0_axis_0"), val = tensor(0)]; + tensor sliding_windows_0_size_0 = const()[name = tensor("sliding_windows_0_size_0"), val = tensor(400)]; + tensor sliding_windows_0_stride_0 = const()[name = tensor("sliding_windows_0_stride_0"), val = tensor(160)]; + tensor sliding_windows_0 = sliding_windows(axis = sliding_windows_0_axis_0, size = sliding_windows_0_size_0, stride = sliding_windows_0_stride_0, x = var_6)[name = tensor("sliding_windows_0")]; + tensor var_42_axes_0 = const()[name = tensor("op_42_axes_0"), val = tensor([1])]; + tensor var_42_keep_dims_0 = const()[name = tensor("op_42_keep_dims_0"), val = tensor(false)]; + tensor var_42 = reduce_mean(axes = var_42_axes_0, keep_dims = var_42_keep_dims_0, x = sliding_windows_0)[name = tensor("op_42")]; + tensor row_means_axes_0 = const()[name = tensor("row_means_axes_0"), val = tensor([1])]; + tensor row_means = expand_dims(axes = row_means_axes_0, x = var_42)[name = tensor("row_means")]; + tensor strided_input_3 = sub(x = sliding_windows_0, y = row_means)[name = tensor("strided_input_3")]; + tensor input_1_axes_0 = const()[name = tensor("input_1_axes_0"), val = tensor([0])]; + tensor input_1 = expand_dims(axes = input_1_axes_0, x = strided_input_3)[name = tensor("input_1")]; + tensor const_2 = const()[name = tensor("const_2"), val = tensor(0x0p+0)]; + tensor var_54_pad_0 = const()[name = tensor("op_54_pad_0"), val = tensor([0, 0, 0, 0, 1, 0])]; + tensor var_54_mode_0 = const()[name = tensor("op_54_mode_0"), val = tensor("replicate")]; + tensor var_54 = pad(constant_val = const_2, mode = var_54_mode_0, pad = var_54_pad_0, x = input_1)[name = tensor("op_54")]; + tensor offset_strided_input_axes_0 = const()[name = tensor("offset_strided_input_axes_0"), val = tensor([0])]; + tensor offset_strided_input = squeeze(axes = offset_strided_input_axes_0, x = var_54)[name = tensor("offset_strided_input")]; + tensor var_66_begin_0 = const()[name = tensor("op_66_begin_0"), val = tensor([0, 0])]; + tensor var_66_end_0 = const()[name = tensor("op_66_end_0"), val = tensor([998, 400])]; + tensor var_66_end_mask_0 = const()[name = tensor("op_66_end_mask_0"), val = tensor([true, false])]; + tensor var_66 = slice_by_index(begin = var_66_begin_0, end = var_66_end_0, end_mask = var_66_end_mask_0, x = offset_strided_input)[name = tensor("op_66")]; + tensor var_67 = const()[name = tensor("op_67"), val = tensor(0x1.f0a3d8p-1)]; + tensor var_68 = mul(x = var_66, y = var_67)[name = tensor("op_68")]; + tensor strided_input_5 = sub(x = strided_input_3, y = var_68)[name = tensor("strided_input_5")]; + tensor window_function = const()[name = tensor("window_function"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor strided_input_7 = mul(x = strided_input_5, y = window_function)[name = tensor("strided_input_7")]; + tensor input_3_axes_0 = const()[name = tensor("input_3_axes_0"), val = tensor([0])]; + tensor input_3 = expand_dims(axes = input_3_axes_0, x = strided_input_7)[name = tensor("input_3")]; + tensor const_3 = const()[name = tensor("const_3"), val = tensor(0x0p+0)]; + tensor var_90_pad_0 = const()[name = tensor("op_90_pad_0"), val = tensor([0, 0, 0, 0, 0, 112])]; + tensor var_90_mode_0 = const()[name = tensor("op_90_mode_0"), val = tensor("constant")]; + tensor var_90 = pad(constant_val = const_3, mode = var_90_mode_0, pad = var_90_pad_0, x = input_3)[name = tensor("op_90")]; + tensor strided_input_axes_0 = const()[name = tensor("strided_input_axes_0"), val = tensor([0])]; + tensor strided_input = squeeze(axes = strided_input_axes_0, x = var_90)[name = tensor("strided_input")]; + tensor cos_0 = const()[name = tensor("cos_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1728)))]; + tensor sin_0 = const()[name = tensor("sin_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1050368)))]; + tensor matmul_1_transpose_x_1 = const()[name = tensor("matmul_1_transpose_x_1"), val = tensor(false)]; + tensor matmul_1_transpose_y_1 = const()[name = tensor("matmul_1_transpose_y_1"), val = tensor(true)]; + tensor matmul_1 = matmul(transpose_x = matmul_1_transpose_x_1, transpose_y = matmul_1_transpose_y_1, x = cos_0, y = strided_input)[name = tensor("matmul_1")]; + tensor matmul_3_transpose_x_1 = const()[name = tensor("matmul_3_transpose_x_1"), val = tensor(false)]; + tensor matmul_3_transpose_y_1 = const()[name = tensor("matmul_3_transpose_y_1"), val = tensor(true)]; + tensor matmul_3 = matmul(transpose_x = matmul_3_transpose_x_1, transpose_y = matmul_3_transpose_y_1, x = sin_0, y = strided_input)[name = tensor("matmul_3")]; + tensor mul_1_y_0 = const()[name = tensor("mul_1_y_0"), val = tensor(-0x1p+0)]; + tensor mul_1 = mul(x = matmul_3, y = mul_1_y_0)[name = tensor("mul_1")]; + tensor transpose_3_perm_0 = const()[name = tensor("transpose_3_perm_0"), val = tensor([-1, 0])]; + tensor transpose_4_perm_0 = const()[name = tensor("transpose_4_perm_0"), val = tensor([-1, 0])]; + tensor range_1d_2 = const()[name = tensor("range_1d_2"), val = tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256])]; + tensor gather_0_axis_0 = const()[name = tensor("gather_0_axis_0"), val = tensor(-1)]; + tensor gather_0_batch_dims_0 = const()[name = tensor("gather_0_batch_dims_0"), val = tensor(0)]; + tensor transpose_3 = transpose(perm = transpose_3_perm_0, x = matmul_1)[name = tensor("transpose_6")]; + tensor gather_0 = gather(axis = gather_0_axis_0, batch_dims = gather_0_batch_dims_0, indices = range_1d_2, x = transpose_3)[name = tensor("gather_0")]; + tensor gather_1_axis_0 = const()[name = tensor("gather_1_axis_0"), val = tensor(-1)]; + tensor gather_1_batch_dims_0 = const()[name = tensor("gather_1_batch_dims_0"), val = tensor(0)]; + tensor transpose_4 = transpose(perm = transpose_4_perm_0, x = mul_1)[name = tensor("transpose_5")]; + tensor gather_1 = gather(axis = gather_1_axis_0, batch_dims = gather_1_batch_dims_0, indices = range_1d_2, x = transpose_4)[name = tensor("gather_1")]; + tensor square_0 = square(x = gather_0)[name = tensor("square_0")]; + tensor square_1 = square(x = gather_1)[name = tensor("square_1")]; + tensor add_1 = add(x = square_0, y = square_1)[name = tensor("add_1")]; + tensor spectrum = identity(x = add_1)[name = tensor("spectrum")]; + tensor mel_energies_3 = const()[name = tensor("mel_energies_3"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2099008)))]; + tensor mel_energies_bias_0 = const()[name = tensor("mel_energies_bias_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2181312)))]; + tensor mel_energies = linear(bias = mel_energies_bias_0, weight = mel_energies_3, x = spectrum)[name = tensor("mel_energies")]; + tensor const_10 = const()[name = tensor("const_10"), val = tensor(0x1p-23)]; + tensor var_186 = maximum(x = mel_energies, y = const_10)[name = tensor("op_186")]; + tensor filter_banks_epsilon_0 = const()[name = tensor("filter_banks_epsilon_0"), val = tensor(0x1p-149)]; + tensor filter_banks = log(epsilon = filter_banks_epsilon_0, x = var_186)[name = tensor("filter_banks")]; + tensor var_192_axes_0 = const()[name = tensor("op_192_axes_0"), val = tensor([0])]; + tensor var_192_keep_dims_0 = const()[name = tensor("op_192_keep_dims_0"), val = tensor(true)]; + tensor var_192 = reduce_mean(axes = var_192_axes_0, keep_dims = var_192_keep_dims_0, x = filter_banks)[name = tensor("op_192")]; + tensor var_194 = sub(x = filter_banks, y = var_192)[name = tensor("op_194")]; + tensor obj_axes_0 = const()[name = tensor("obj_axes_0"), val = tensor([0])]; + tensor preprocessor_output_1_type_fp32 = expand_dims(axes = obj_axes_0, x = var_194)[name = tensor("obj")]; + tensor cast_9_dtype_0 = const()[name = tensor("cast_9_dtype_0"), val = tensor("fp16")]; + tensor preprocessor_output_1 = cast(dtype = cast_9_dtype_0, x = preprocessor_output_1_type_fp32)[name = tensor("cast_10")]; + } -> (preprocessor_output_1); +} \ No newline at end of file diff --git a/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedderPreprocessor.mlmodelc/weights/weight.bin b/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedderPreprocessor.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..a370d87e4d244f921e8f79574bc5385907b9bb29 --- /dev/null +++ b/speaker_embedder/pyannote-v3/W8A16/SpeakerEmbedderPreprocessor.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5f2c284bd22f1f7ab76901c1c6e57f82d4ebbf057fa0b924aad057f124f77a89 +size 2181696 diff --git a/speaker_segmenter/pyannote-v3/W32A32/LICENSE_NOTICE.txt b/speaker_segmenter/pyannote-v3/W32A32/LICENSE_NOTICE.txt new file mode 100644 index 0000000000000000000000000000000000000000..be2da6c6e6d746ab53f1b21eac16d611aed1193a --- /dev/null +++ b/speaker_segmenter/pyannote-v3/W32A32/LICENSE_NOTICE.txt @@ -0,0 +1,7 @@ +Argmax proprietary and confidential. Under NDA. + +Copyright 2024 Argmax, Inc. All rights reserved. + +Unauthorized access, copying, use, distribution, and or commercialization of this file, via any medium or means is strictly prohibited. + +Please contact Argmax for licensing information at info@argmaxinc.com. diff --git a/speaker_segmenter/pyannote-v3/W32A32/README.txt b/speaker_segmenter/pyannote-v3/W32A32/README.txt new file mode 100644 index 0000000000000000000000000000000000000000..b64b4cbd04f27df7f9a363b2379b86c4522db1ef --- /dev/null +++ b/speaker_segmenter/pyannote-v3/W32A32/README.txt @@ -0,0 +1,6 @@ +# License + +Original model weights: https://huggingface.co/pyannote/segmentation-3.0/blob/main/LICENSE +Argmax-optimized model asset (Assets with `.mlmodelc` extension): https://huggingface.co/argmaxinc/speakerkit-pro/blob/main/LICENSE_NOTICE.txt + +Please contact info@argmaxinc.com for licensing SpeakerKit Pro assets \ No newline at end of file diff --git a/speaker_segmenter/pyannote-v3/W32A32/SpeakerSegmenter.mlmodelc/LICENSE_NOTICE.txt b/speaker_segmenter/pyannote-v3/W32A32/SpeakerSegmenter.mlmodelc/LICENSE_NOTICE.txt new file mode 100644 index 0000000000000000000000000000000000000000..be2da6c6e6d746ab53f1b21eac16d611aed1193a --- /dev/null +++ b/speaker_segmenter/pyannote-v3/W32A32/SpeakerSegmenter.mlmodelc/LICENSE_NOTICE.txt @@ -0,0 +1,7 @@ +Argmax proprietary and confidential. Under NDA. + +Copyright 2024 Argmax, Inc. All rights reserved. + +Unauthorized access, copying, use, distribution, and or commercialization of this file, via any medium or means is strictly prohibited. + +Please contact Argmax for licensing information at info@argmaxinc.com. diff --git a/speaker_segmenter/pyannote-v3/SpeakerSegmenter.mlmodelc/analytics/coremldata.bin b/speaker_segmenter/pyannote-v3/W32A32/SpeakerSegmenter.mlmodelc/analytics/coremldata.bin similarity index 100% rename from speaker_segmenter/pyannote-v3/SpeakerSegmenter.mlmodelc/analytics/coremldata.bin rename to speaker_segmenter/pyannote-v3/W32A32/SpeakerSegmenter.mlmodelc/analytics/coremldata.bin diff --git a/speaker_segmenter/pyannote-v3/SpeakerSegmenter.mlmodelc/coremldata.bin b/speaker_segmenter/pyannote-v3/W32A32/SpeakerSegmenter.mlmodelc/coremldata.bin similarity index 100% rename from speaker_segmenter/pyannote-v3/SpeakerSegmenter.mlmodelc/coremldata.bin rename to speaker_segmenter/pyannote-v3/W32A32/SpeakerSegmenter.mlmodelc/coremldata.bin diff --git a/speaker_segmenter/pyannote-v3/SpeakerSegmenter.mlmodelc/metadata.json b/speaker_segmenter/pyannote-v3/W32A32/SpeakerSegmenter.mlmodelc/metadata.json similarity index 100% rename from speaker_segmenter/pyannote-v3/SpeakerSegmenter.mlmodelc/metadata.json rename to speaker_segmenter/pyannote-v3/W32A32/SpeakerSegmenter.mlmodelc/metadata.json diff --git a/speaker_segmenter/pyannote-v3/SpeakerSegmenter.mlmodelc/model.mil b/speaker_segmenter/pyannote-v3/W32A32/SpeakerSegmenter.mlmodelc/model.mil similarity index 100% rename from speaker_segmenter/pyannote-v3/SpeakerSegmenter.mlmodelc/model.mil rename to speaker_segmenter/pyannote-v3/W32A32/SpeakerSegmenter.mlmodelc/model.mil diff --git a/speaker_segmenter/pyannote-v3/SpeakerSegmenter.mlmodelc/weights/weight.bin b/speaker_segmenter/pyannote-v3/W32A32/SpeakerSegmenter.mlmodelc/weights/weight.bin similarity index 100% rename from speaker_segmenter/pyannote-v3/SpeakerSegmenter.mlmodelc/weights/weight.bin rename to speaker_segmenter/pyannote-v3/W32A32/SpeakerSegmenter.mlmodelc/weights/weight.bin diff --git a/speaker_segmenter/pyannote-v3/W8A16/LICENSE_NOTICE.txt b/speaker_segmenter/pyannote-v3/W8A16/LICENSE_NOTICE.txt new file mode 100644 index 0000000000000000000000000000000000000000..be2da6c6e6d746ab53f1b21eac16d611aed1193a --- /dev/null +++ b/speaker_segmenter/pyannote-v3/W8A16/LICENSE_NOTICE.txt @@ -0,0 +1,7 @@ +Argmax proprietary and confidential. Under NDA. + +Copyright 2024 Argmax, Inc. All rights reserved. + +Unauthorized access, copying, use, distribution, and or commercialization of this file, via any medium or means is strictly prohibited. + +Please contact Argmax for licensing information at info@argmaxinc.com. diff --git a/speaker_segmenter/pyannote-v3/W8A16/README.txt b/speaker_segmenter/pyannote-v3/W8A16/README.txt new file mode 100644 index 0000000000000000000000000000000000000000..b64b4cbd04f27df7f9a363b2379b86c4522db1ef --- /dev/null +++ b/speaker_segmenter/pyannote-v3/W8A16/README.txt @@ -0,0 +1,6 @@ +# License + +Original model weights: https://huggingface.co/pyannote/segmentation-3.0/blob/main/LICENSE +Argmax-optimized model asset (Assets with `.mlmodelc` extension): https://huggingface.co/argmaxinc/speakerkit-pro/blob/main/LICENSE_NOTICE.txt + +Please contact info@argmaxinc.com for licensing SpeakerKit Pro assets \ No newline at end of file diff --git a/speaker_segmenter/pyannote-v3/W8A16/SpeakerSegmenter.mlmodelc/analytics/coremldata.bin b/speaker_segmenter/pyannote-v3/W8A16/SpeakerSegmenter.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..98c976f33e885b8cf39b6fa0756e8e7d90a68ff6 --- /dev/null +++ b/speaker_segmenter/pyannote-v3/W8A16/SpeakerSegmenter.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:40637aa0cb2a073bc303c7ca9ee79da35fa81d2cad1ead180e93b134005b95de +size 243 diff --git a/speaker_segmenter/pyannote-v3/W8A16/SpeakerSegmenter.mlmodelc/coremldata.bin b/speaker_segmenter/pyannote-v3/W8A16/SpeakerSegmenter.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..a0479d50b1a58a324a6e4bbb8d88d7b2ed52ab94 --- /dev/null +++ b/speaker_segmenter/pyannote-v3/W8A16/SpeakerSegmenter.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6c356ed983b2a3332ce51299ca0f9747a35cb6c2a67b0ac24c69dbef3f989634 +size 497 diff --git a/speaker_segmenter/pyannote-v3/W8A16/SpeakerSegmenter.mlmodelc/metadata.json b/speaker_segmenter/pyannote-v3/W8A16/SpeakerSegmenter.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..8c71ca244a2d67d993ac9c2a6aa2dafd97a9522c --- /dev/null +++ b/speaker_segmenter/pyannote-v3/W8A16/SpeakerSegmenter.mlmodelc/metadata.json @@ -0,0 +1,133 @@ +[ + { + "metadataOutputVersion" : "3.0", + "storagePrecision" : "Mixed (Float16, Palettized (8 bits))", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 21 × 589 × 3)", + "shortDescription" : "", + "shape" : "[21, 589, 3]", + "name" : "speaker_probs", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 21 × 589 × 3)", + "shortDescription" : "", + "shape" : "[21, 589, 3]", + "name" : "speaker_ids", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 21 × 3)", + "shortDescription" : "", + "shape" : "[21, 3]", + "name" : "speaker_activity", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 21 × 589)", + "shortDescription" : "", + "shape" : "[21, 589]", + "name" : "overlapped_speaker_activity", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 1767)", + "shortDescription" : "", + "shape" : "[1767]", + "name" : "voice_activity", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 21 × 1 × 160000)", + "shortDescription" : "", + "shape" : "[21, 1, 160000]", + "name" : "sliding_window_waveform", + "type" : "MultiArray" + } + ], + "modelParameters" : [ + + ], + "specificationVersion" : 8, + "mlProgramOperationTypeHistogram" : { + "Ios17.reduceArgmax" : 1, + "Ios16.maxPool" : 3, + "Ios17.slidingWindows" : 1, + "Ios17.instanceNorm" : 4, + "Ios17.exp" : 1, + "Ios16.softmax" : 1, + "Ios17.scatter" : 42, + "Ios17.transpose" : 2, + "Ios17.expandDims" : 1, + "Ios16.reduceMax" : 1, + "Ios17.add" : 40, + "Ios17.sliceByIndex" : 61, + "Ios16.reduceSum" : 2, + "Ios17.log" : 1, + "Ios17.conv" : 3, + "Ios17.lstm" : 4, + "Ios16.constexprLutToDense" : 22, + "OneHot" : 1, + "Ios17.cast" : 2, + "Ios17.linear" : 5, + "Ios17.leakyRelu" : 5, + "Ios17.abs" : 1, + "Ios17.realDiv" : 1, + "Ios17.greater" : 1 + }, + "computePrecision" : "Mixed (Float16, Float32, Int32)", + "isUpdatable" : "0", + "stateSchema" : [ + + ], + "availability" : { + "macOS" : "14.0", + "tvOS" : "17.0", + "visionOS" : "1.0", + "watchOS" : "10.0", + "iOS" : "17.0", + "macCatalyst" : "17.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "userDefinedMetadata" : { + "com.github.apple.coremltools.source_dialect" : "TorchScript", + "com.github.apple.coremltools.source" : "torch==2.5.1", + "com.github.apple.coremltools.version" : "8.1" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 480000)", + "shortDescription" : "", + "shape" : "[480000]", + "name" : "waveform", + "type" : "MultiArray" + } + ], + "generatedClassName" : "SpeakerSegmenter_8_bit", + "method" : "predict" + } +] \ No newline at end of file diff --git a/speaker_segmenter/pyannote-v3/W8A16/SpeakerSegmenter.mlmodelc/model.mil b/speaker_segmenter/pyannote-v3/W8A16/SpeakerSegmenter.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..158ce216548ccddd75173c13609948457715cc92 --- /dev/null +++ b/speaker_segmenter/pyannote-v3/W8A16/SpeakerSegmenter.mlmodelc/model.mil @@ -0,0 +1,687 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3401.3.1"}, {"coremlc-version", "3401.4.1"}})] +{ + func main(tensor waveform) { + tensor var_3 = const()[name = tensor("op_3"), val = tensor(0)]; + tensor sliding_windows_0_size_0 = const()[name = tensor("sliding_windows_0_size_0"), val = tensor(160000)]; + tensor sliding_windows_0_stride_0 = const()[name = tensor("sliding_windows_0_stride_0"), val = tensor(16000)]; + tensor sliding_windows_0_cast_fp16 = sliding_windows(axis = var_3, size = sliding_windows_0_size_0, stride = sliding_windows_0_stride_0, x = waveform)[name = tensor("sliding_windows_0_cast_fp16")]; + tensor input_1_axes_0 = const()[name = tensor("input_1_axes_0"), val = tensor([1])]; + tensor sliding_window_waveform = expand_dims(axes = input_1_axes_0, x = sliding_windows_0_cast_fp16)[name = tensor("input_1_cast_fp16")]; + tensor var_9 = const()[name = tensor("op_9"), val = tensor(-1)]; + tensor var_26 = const()[name = tensor("op_26"), val = tensor(0x1.47ae14p-7)]; + tensor model_sincnet_wav_norm1d_weight_to_fp16 = const()[name = tensor("model_sincnet_wav_norm1d_weight_to_fp16"), val = tensor([0x1.44p-7])]; + tensor model_sincnet_wav_norm1d_bias_to_fp16 = const()[name = tensor("model_sincnet_wav_norm1d_bias_to_fp16"), val = tensor([0x1.734p-5])]; + tensor var_17_to_fp16 = const()[name = tensor("op_17_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_3_cast_fp16 = instance_norm(beta = model_sincnet_wav_norm1d_bias_to_fp16, epsilon = var_17_to_fp16, gamma = model_sincnet_wav_norm1d_weight_to_fp16, x = sliding_window_waveform)[name = tensor("input_3_cast_fp16")]; + tensor outputs_pad_type_0 = const()[name = tensor("outputs_pad_type_0"), val = tensor("valid")]; + tensor outputs_strides_0 = const()[name = tensor("outputs_strides_0"), val = tensor([10])]; + tensor outputs_pad_0 = const()[name = tensor("outputs_pad_0"), val = tensor([0, 0])]; + tensor outputs_dilations_0 = const()[name = tensor("outputs_dilations_0"), val = tensor([1])]; + tensor outputs_groups_0 = const()[name = tensor("outputs_groups_0"), val = tensor(1)]; + tensor model_sincnet_conv1d_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20224))), name = tensor("model_sincnet_conv1d_0_weight_to_fp16_palettized"), shape = tensor([80, 1, 251])]; + tensor outputs_cast_fp16 = conv(dilations = outputs_dilations_0, groups = outputs_groups_0, pad = outputs_pad_0, pad_type = outputs_pad_type_0, strides = outputs_strides_0, weight = model_sincnet_conv1d_0_weight_to_fp16_palettized, x = input_3_cast_fp16)[name = tensor("outputs_cast_fp16")]; + tensor input_5_cast_fp16 = abs(x = outputs_cast_fp16)[name = tensor("input_5_cast_fp16")]; + tensor var_57 = const()[name = tensor("op_57"), val = tensor([3])]; + tensor var_58 = const()[name = tensor("op_58"), val = tensor([3])]; + tensor input_7_pad_type_0 = const()[name = tensor("input_7_pad_type_0"), val = tensor("custom")]; + tensor input_7_pad_0 = const()[name = tensor("input_7_pad_0"), val = tensor([0, 0])]; + tensor input_7_ceil_mode_0 = const()[name = tensor("input_7_ceil_mode_0"), val = tensor(false)]; + tensor input_7_cast_fp16 = max_pool(ceil_mode = input_7_ceil_mode_0, kernel_sizes = var_57, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = var_58, x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; + tensor model_sincnet_norm1d_0_weight_to_fp16 = const()[name = tensor("model_sincnet_norm1d_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20800)))]; + tensor model_sincnet_norm1d_0_bias_to_fp16 = const()[name = tensor("model_sincnet_norm1d_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21056)))]; + tensor input_9_cast_fp16 = instance_norm(beta = model_sincnet_norm1d_0_bias_to_fp16, epsilon = var_17_to_fp16, gamma = model_sincnet_norm1d_0_weight_to_fp16, x = input_7_cast_fp16)[name = tensor("input_9_cast_fp16")]; + tensor input_11_cast_fp16 = leaky_relu(alpha = var_26, x = input_9_cast_fp16)[name = tensor("input_11_cast_fp16")]; + tensor input_13_pad_type_0 = const()[name = tensor("input_13_pad_type_0"), val = tensor("valid")]; + tensor input_13_strides_0 = const()[name = tensor("input_13_strides_0"), val = tensor([1])]; + tensor input_13_pad_0 = const()[name = tensor("input_13_pad_0"), val = tensor([0, 0])]; + tensor input_13_dilations_0 = const()[name = tensor("input_13_dilations_0"), val = tensor([1])]; + tensor input_13_groups_0 = const()[name = tensor("input_13_groups_0"), val = tensor(1)]; + tensor model_sincnet_conv1d_1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21312))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45376))), name = tensor("model_sincnet_conv1d_1_weight_to_fp16_palettized"), shape = tensor([60, 80, 5])]; + tensor model_sincnet_conv1d_1_bias_to_fp16 = const()[name = tensor("model_sincnet_conv1d_1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45952)))]; + tensor input_13_cast_fp16 = conv(bias = model_sincnet_conv1d_1_bias_to_fp16, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = model_sincnet_conv1d_1_weight_to_fp16_palettized, x = input_11_cast_fp16)[name = tensor("input_13_cast_fp16")]; + tensor var_73 = const()[name = tensor("op_73"), val = tensor([3])]; + tensor var_74 = const()[name = tensor("op_74"), val = tensor([3])]; + tensor input_15_pad_type_0 = const()[name = tensor("input_15_pad_type_0"), val = tensor("custom")]; + tensor input_15_pad_0 = const()[name = tensor("input_15_pad_0"), val = tensor([0, 0])]; + tensor input_15_ceil_mode_0 = const()[name = tensor("input_15_ceil_mode_0"), val = tensor(false)]; + tensor input_15_cast_fp16 = max_pool(ceil_mode = input_15_ceil_mode_0, kernel_sizes = var_73, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = var_74, x = input_13_cast_fp16)[name = tensor("input_15_cast_fp16")]; + tensor model_sincnet_norm1d_1_weight_to_fp16 = const()[name = tensor("model_sincnet_norm1d_1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46144)))]; + tensor model_sincnet_norm1d_1_bias_to_fp16 = const()[name = tensor("model_sincnet_norm1d_1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46336)))]; + tensor input_17_cast_fp16 = instance_norm(beta = model_sincnet_norm1d_1_bias_to_fp16, epsilon = var_17_to_fp16, gamma = model_sincnet_norm1d_1_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("input_17_cast_fp16")]; + tensor input_19_cast_fp16 = leaky_relu(alpha = var_26, x = input_17_cast_fp16)[name = tensor("input_19_cast_fp16")]; + tensor input_21_pad_type_0 = const()[name = tensor("input_21_pad_type_0"), val = tensor("valid")]; + tensor input_21_strides_0 = const()[name = tensor("input_21_strides_0"), val = tensor([1])]; + tensor input_21_pad_0 = const()[name = tensor("input_21_pad_0"), val = tensor([0, 0])]; + tensor input_21_dilations_0 = const()[name = tensor("input_21_dilations_0"), val = tensor([1])]; + tensor input_21_groups_0 = const()[name = tensor("input_21_groups_0"), val = tensor(1)]; + tensor model_sincnet_conv1d_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64640))), name = tensor("model_sincnet_conv1d_2_weight_to_fp16_palettized"), shape = tensor([60, 60, 5])]; + tensor model_sincnet_conv1d_2_bias_to_fp16 = const()[name = tensor("model_sincnet_conv1d_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65216)))]; + tensor input_21_cast_fp16 = conv(bias = model_sincnet_conv1d_2_bias_to_fp16, dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = model_sincnet_conv1d_2_weight_to_fp16_palettized, x = input_19_cast_fp16)[name = tensor("input_21_cast_fp16")]; + tensor var_89 = const()[name = tensor("op_89"), val = tensor([3])]; + tensor var_90 = const()[name = tensor("op_90"), val = tensor([3])]; + tensor input_23_pad_type_0 = const()[name = tensor("input_23_pad_type_0"), val = tensor("custom")]; + tensor input_23_pad_0 = const()[name = tensor("input_23_pad_0"), val = tensor([0, 0])]; + tensor input_23_ceil_mode_0 = const()[name = tensor("input_23_ceil_mode_0"), val = tensor(false)]; + tensor input_23_cast_fp16 = max_pool(ceil_mode = input_23_ceil_mode_0, kernel_sizes = var_89, pad = input_23_pad_0, pad_type = input_23_pad_type_0, strides = var_90, x = input_21_cast_fp16)[name = tensor("input_23_cast_fp16")]; + tensor model_sincnet_norm1d_2_weight_to_fp16 = const()[name = tensor("model_sincnet_norm1d_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65408)))]; + tensor model_sincnet_norm1d_2_bias_to_fp16 = const()[name = tensor("model_sincnet_norm1d_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65600)))]; + tensor input_25_cast_fp16 = instance_norm(beta = model_sincnet_norm1d_2_bias_to_fp16, epsilon = var_17_to_fp16, gamma = model_sincnet_norm1d_2_weight_to_fp16, x = input_23_cast_fp16)[name = tensor("input_25_cast_fp16")]; + tensor x_cast_fp16 = leaky_relu(alpha = var_26, x = input_25_cast_fp16)[name = tensor("x_cast_fp16")]; + tensor transpose_2_perm_0 = const()[name = tensor("transpose_2_perm_0"), val = tensor([2, 0, 1])]; + tensor input_29_lstm_layer_0_direction_0 = const()[name = tensor("input_29_lstm_layer_0_direction_0"), val = tensor("bidirectional")]; + tensor input_29_lstm_layer_0_output_sequence_0 = const()[name = tensor("input_29_lstm_layer_0_output_sequence_0"), val = tensor(true)]; + tensor input_29_lstm_layer_0_recurrent_activation_0 = const()[name = tensor("input_29_lstm_layer_0_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_29_lstm_layer_0_cell_activation_0 = const()[name = tensor("input_29_lstm_layer_0_cell_activation_0"), val = tensor("tanh")]; + tensor input_29_lstm_layer_0_activation_0 = const()[name = tensor("input_29_lstm_layer_0_activation_0"), val = tensor("tanh")]; + tensor input_29_lstm_layer_0_lstm_h0_reshaped_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71232))), name = tensor("input_29_lstm_layer_0_lstm_h0_reshaped_to_fp16_palettized"), shape = tensor([21, 256])]; + tensor concat_4_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(71808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(102592))), name = tensor("concat_4_to_fp16_palettized"), shape = tensor([512, 60])]; + tensor concat_5_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(103168))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(168768))), name = tensor("concat_5_to_fp16_palettized"), shape = tensor([512, 128])]; + tensor add_0_to_fp16 = const()[name = tensor("add_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169344)))]; + tensor concat_6_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(170432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201216))), name = tensor("concat_6_to_fp16_palettized"), shape = tensor([512, 60])]; + tensor concat_7_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267392))), name = tensor("concat_7_to_fp16_palettized"), shape = tensor([512, 128])]; + tensor add_1_to_fp16 = const()[name = tensor("add_1_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267968)))]; + tensor transpose_2_cast_fp16 = transpose(perm = transpose_2_perm_0, x = x_cast_fp16)[name = tensor("transpose_1")]; + tensor input_29_lstm_layer_0_cast_fp16_0, tensor input_29_lstm_layer_0_cast_fp16_1, tensor input_29_lstm_layer_0_cast_fp16_2 = lstm(activation = input_29_lstm_layer_0_activation_0, bias = add_0_to_fp16, bias_back = add_1_to_fp16, cell_activation = input_29_lstm_layer_0_cell_activation_0, direction = input_29_lstm_layer_0_direction_0, initial_c = input_29_lstm_layer_0_lstm_h0_reshaped_to_fp16_palettized, initial_h = input_29_lstm_layer_0_lstm_h0_reshaped_to_fp16_palettized, output_sequence = input_29_lstm_layer_0_output_sequence_0, recurrent_activation = input_29_lstm_layer_0_recurrent_activation_0, weight_hh = concat_5_to_fp16_palettized, weight_hh_back = concat_7_to_fp16_palettized, weight_ih = concat_4_to_fp16_palettized, weight_ih_back = concat_6_to_fp16_palettized, x = transpose_2_cast_fp16)[name = tensor("input_29_lstm_layer_0_cast_fp16")]; + tensor input_29_lstm_layer_1_direction_0 = const()[name = tensor("input_29_lstm_layer_1_direction_0"), val = tensor("bidirectional")]; + tensor input_29_lstm_layer_1_output_sequence_0 = const()[name = tensor("input_29_lstm_layer_1_output_sequence_0"), val = tensor(true)]; + tensor input_29_lstm_layer_1_recurrent_activation_0 = const()[name = tensor("input_29_lstm_layer_1_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_29_lstm_layer_1_cell_activation_0 = const()[name = tensor("input_29_lstm_layer_1_cell_activation_0"), val = tensor("tanh")]; + tensor input_29_lstm_layer_1_activation_0 = const()[name = tensor("input_29_lstm_layer_1_activation_0"), val = tensor("tanh")]; + tensor concat_14_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(269056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(400192))), name = tensor("concat_14_to_fp16_palettized"), shape = tensor([512, 256])]; + tensor concat_15_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(400768))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(466368))), name = tensor("concat_15_to_fp16_palettized"), shape = tensor([512, 128])]; + tensor add_2_to_fp16 = const()[name = tensor("add_2_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(466944)))]; + tensor concat_16_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(468032))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(599168))), name = tensor("concat_16_to_fp16_palettized"), shape = tensor([512, 256])]; + tensor concat_17_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(599744))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(665344))), name = tensor("concat_17_to_fp16_palettized"), shape = tensor([512, 128])]; + tensor add_3_to_fp16 = const()[name = tensor("add_3_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(665920)))]; + tensor input_29_lstm_layer_1_cast_fp16_0, tensor input_29_lstm_layer_1_cast_fp16_1, tensor input_29_lstm_layer_1_cast_fp16_2 = lstm(activation = input_29_lstm_layer_1_activation_0, bias = add_2_to_fp16, bias_back = add_3_to_fp16, cell_activation = input_29_lstm_layer_1_cell_activation_0, direction = input_29_lstm_layer_1_direction_0, initial_c = input_29_lstm_layer_0_lstm_h0_reshaped_to_fp16_palettized, initial_h = input_29_lstm_layer_0_lstm_h0_reshaped_to_fp16_palettized, output_sequence = input_29_lstm_layer_1_output_sequence_0, recurrent_activation = input_29_lstm_layer_1_recurrent_activation_0, weight_hh = concat_15_to_fp16_palettized, weight_hh_back = concat_17_to_fp16_palettized, weight_ih = concat_14_to_fp16_palettized, weight_ih_back = concat_16_to_fp16_palettized, x = input_29_lstm_layer_0_cast_fp16_0)[name = tensor("input_29_lstm_layer_1_cast_fp16")]; + tensor input_29_lstm_layer_2_direction_0 = const()[name = tensor("input_29_lstm_layer_2_direction_0"), val = tensor("bidirectional")]; + tensor input_29_lstm_layer_2_output_sequence_0 = const()[name = tensor("input_29_lstm_layer_2_output_sequence_0"), val = tensor(true)]; + tensor input_29_lstm_layer_2_recurrent_activation_0 = const()[name = tensor("input_29_lstm_layer_2_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_29_lstm_layer_2_cell_activation_0 = const()[name = tensor("input_29_lstm_layer_2_cell_activation_0"), val = tensor("tanh")]; + tensor input_29_lstm_layer_2_activation_0 = const()[name = tensor("input_29_lstm_layer_2_activation_0"), val = tensor("tanh")]; + tensor concat_24_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(667008))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(798144))), name = tensor("concat_24_to_fp16_palettized"), shape = tensor([512, 256])]; + tensor concat_25_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(798720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(864320))), name = tensor("concat_25_to_fp16_palettized"), shape = tensor([512, 128])]; + tensor add_4_to_fp16 = const()[name = tensor("add_4_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(864896)))]; + tensor concat_26_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(865984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(997120))), name = tensor("concat_26_to_fp16_palettized"), shape = tensor([512, 256])]; + tensor concat_27_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(997696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1063296))), name = tensor("concat_27_to_fp16_palettized"), shape = tensor([512, 128])]; + tensor add_5_to_fp16 = const()[name = tensor("add_5_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1063872)))]; + tensor input_29_lstm_layer_2_cast_fp16_0, tensor input_29_lstm_layer_2_cast_fp16_1, tensor input_29_lstm_layer_2_cast_fp16_2 = lstm(activation = input_29_lstm_layer_2_activation_0, bias = add_4_to_fp16, bias_back = add_5_to_fp16, cell_activation = input_29_lstm_layer_2_cell_activation_0, direction = input_29_lstm_layer_2_direction_0, initial_c = input_29_lstm_layer_0_lstm_h0_reshaped_to_fp16_palettized, initial_h = input_29_lstm_layer_0_lstm_h0_reshaped_to_fp16_palettized, output_sequence = input_29_lstm_layer_2_output_sequence_0, recurrent_activation = input_29_lstm_layer_2_recurrent_activation_0, weight_hh = concat_25_to_fp16_palettized, weight_hh_back = concat_27_to_fp16_palettized, weight_ih = concat_24_to_fp16_palettized, weight_ih_back = concat_26_to_fp16_palettized, x = input_29_lstm_layer_1_cast_fp16_0)[name = tensor("input_29_lstm_layer_2_cast_fp16")]; + tensor input_29_batch_first_direction_0 = const()[name = tensor("input_29_batch_first_direction_0"), val = tensor("bidirectional")]; + tensor input_29_batch_first_output_sequence_0 = const()[name = tensor("input_29_batch_first_output_sequence_0"), val = tensor(true)]; + tensor input_29_batch_first_recurrent_activation_0 = const()[name = tensor("input_29_batch_first_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_29_batch_first_cell_activation_0 = const()[name = tensor("input_29_batch_first_cell_activation_0"), val = tensor("tanh")]; + tensor input_29_batch_first_activation_0 = const()[name = tensor("input_29_batch_first_activation_0"), val = tensor("tanh")]; + tensor concat_34_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1064960))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1196096))), name = tensor("concat_34_to_fp16_palettized"), shape = tensor([512, 256])]; + tensor concat_35_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1196672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1262272))), name = tensor("concat_35_to_fp16_palettized"), shape = tensor([512, 128])]; + tensor add_6_to_fp16 = const()[name = tensor("add_6_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1262848)))]; + tensor concat_36_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1263936))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1395072))), name = tensor("concat_36_to_fp16_palettized"), shape = tensor([512, 256])]; + tensor concat_37_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1395648))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1461248))), name = tensor("concat_37_to_fp16_palettized"), shape = tensor([512, 128])]; + tensor add_7_to_fp16 = const()[name = tensor("add_7_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1461824)))]; + tensor input_29_batch_first_cast_fp16_0, tensor input_29_batch_first_cast_fp16_1, tensor input_29_batch_first_cast_fp16_2 = lstm(activation = input_29_batch_first_activation_0, bias = add_6_to_fp16, bias_back = add_7_to_fp16, cell_activation = input_29_batch_first_cell_activation_0, direction = input_29_batch_first_direction_0, initial_c = input_29_lstm_layer_0_lstm_h0_reshaped_to_fp16_palettized, initial_h = input_29_lstm_layer_0_lstm_h0_reshaped_to_fp16_palettized, output_sequence = input_29_batch_first_output_sequence_0, recurrent_activation = input_29_batch_first_recurrent_activation_0, weight_hh = concat_35_to_fp16_palettized, weight_hh_back = concat_37_to_fp16_palettized, weight_ih = concat_34_to_fp16_palettized, weight_ih_back = concat_36_to_fp16_palettized, x = input_29_lstm_layer_2_cast_fp16_0)[name = tensor("input_29_batch_first_cast_fp16")]; + tensor input_29_perm_0 = const()[name = tensor("input_29_perm_0"), val = tensor([1, 0, 2])]; + tensor model_linear_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1462912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1495744))), name = tensor("model_linear_0_weight_to_fp16_palettized"), shape = tensor([128, 256])]; + tensor model_linear_0_bias_to_fp16 = const()[name = tensor("model_linear_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1496320)))]; + tensor input_29_cast_fp16 = transpose(perm = input_29_perm_0, x = input_29_batch_first_cast_fp16_0)[name = tensor("transpose_0")]; + tensor linear_0_cast_fp16 = linear(bias = model_linear_0_bias_to_fp16, weight = model_linear_0_weight_to_fp16_palettized, x = input_29_cast_fp16)[name = tensor("linear_0_cast_fp16")]; + tensor input_33_cast_fp16 = leaky_relu(alpha = var_26, x = linear_0_cast_fp16)[name = tensor("input_33_cast_fp16")]; + tensor model_linear_1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1496640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1513088))), name = tensor("model_linear_1_weight_to_fp16_palettized"), shape = tensor([128, 128])]; + tensor model_linear_1_bias_to_fp16 = const()[name = tensor("model_linear_1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1513664)))]; + tensor linear_1_cast_fp16 = linear(bias = model_linear_1_bias_to_fp16, weight = model_linear_1_weight_to_fp16_palettized, x = input_33_cast_fp16)[name = tensor("linear_1_cast_fp16")]; + tensor input_37_cast_fp16 = leaky_relu(alpha = var_26, x = linear_1_cast_fp16)[name = tensor("input_37_cast_fp16")]; + tensor model_classifier_weight_to_fp16 = const()[name = tensor("model_classifier_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1513984)))]; + tensor model_classifier_bias_to_fp16 = const()[name = tensor("model_classifier_bias_to_fp16"), val = tensor([-0x1.01p+0, 0x1.67cp-2, 0x1.3d8p-1, 0x1.c8cp-2, -0x1.444p-2, -0x1.59p-1, -0x1.8fcp-2])]; + tensor linear_2_cast_fp16 = linear(bias = model_classifier_bias_to_fp16, weight = model_classifier_weight_to_fp16, x = input_37_cast_fp16)[name = tensor("linear_2_cast_fp16")]; + tensor powerset_softmax_cast_fp16 = softmax(axis = var_9, x = linear_2_cast_fp16)[name = tensor("powerset_softmax_cast_fp16")]; + tensor powerset_epsilon_0 = const()[name = tensor("powerset_epsilon_0"), val = tensor(0x1p-149)]; + tensor powerset_cast_fp16 = log(epsilon = powerset_epsilon_0, x = powerset_softmax_cast_fp16)[name = tensor("powerset_cast_fp16")]; + tensor powerset_probs_1_cast_fp16 = exp(x = powerset_cast_fp16)[name = tensor("powerset_probs_1_cast_fp16")]; + tensor transpose_0_to_fp16 = const()[name = tensor("transpose_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1515840)))]; + tensor speaker_probs_bias_0_to_fp16 = const()[name = tensor("speaker_probs_bias_0_to_fp16"), val = tensor([0x0p+0, 0x0p+0, 0x0p+0])]; + tensor speaker_probs = linear(bias = speaker_probs_bias_0_to_fp16, weight = transpose_0_to_fp16, x = powerset_probs_1_cast_fp16)[name = tensor("speaker_probs_cast_fp16")]; + tensor var_157 = const()[name = tensor("op_157"), val = tensor(-1)]; + tensor var_158 = const()[name = tensor("op_158"), val = tensor(false)]; + tensor var_159_output_dtype_0 = const()[name = tensor("op_159_output_dtype_0"), val = tensor("int32")]; + tensor var_159_cast_fp16 = reduce_argmax(axis = var_157, keep_dims = var_158, output_dtype = var_159_output_dtype_0, x = powerset_cast_fp16)[name = tensor("op_159_cast_fp16")]; + tensor var_161_one_hot_vector_size_0 = const()[name = tensor("op_161_one_hot_vector_size_0"), val = tensor(7)]; + tensor var_161_axis_0 = const()[name = tensor("op_161_axis_0"), val = tensor(-1)]; + tensor var_161_on_value_0 = const()[name = tensor("op_161_on_value_0"), val = tensor(1)]; + tensor var_161_off_value_0 = const()[name = tensor("op_161_off_value_0"), val = tensor(0)]; + tensor var_161 = one_hot(axis = var_161_axis_0, indices = var_159_cast_fp16, off_value = var_161_off_value_0, on_value = var_161_on_value_0, one_hot_vector_size = var_161_one_hot_vector_size_0)[name = tensor("op_161")]; + tensor cast_1_to_fp16_dtype_0 = const()[name = tensor("cast_1_to_fp16_dtype_0"), val = tensor("fp16")]; + tensor transpose_1_to_fp16 = const()[name = tensor("transpose_1_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1515968)))]; + tensor speaker_ids_bias_0_to_fp16 = const()[name = tensor("speaker_ids_bias_0_to_fp16"), val = tensor([0x0p+0, 0x0p+0, 0x0p+0])]; + tensor var_161_to_fp16 = cast(dtype = cast_1_to_fp16_dtype_0, x = var_161)[name = tensor("cast_1")]; + tensor speaker_ids = linear(bias = speaker_ids_bias_0_to_fp16, weight = transpose_1_to_fp16, x = var_161_to_fp16)[name = tensor("speaker_ids_cast_fp16")]; + tensor reduce_max_0_axes_0 = const()[name = tensor("reduce_max_0_axes_0"), val = tensor([-1])]; + tensor reduce_max_0_keep_dims_0 = const()[name = tensor("reduce_max_0_keep_dims_0"), val = tensor(false)]; + tensor reduce_max_0_cast_fp16 = reduce_max(axes = reduce_max_0_axes_0, keep_dims = reduce_max_0_keep_dims_0, x = speaker_probs)[name = tensor("reduce_max_0_cast_fp16")]; + tensor var_202_begin_0 = const()[name = tensor("op_202_begin_0"), val = tensor([0, 0])]; + tensor var_202_end_0 = const()[name = tensor("op_202_end_0"), val = tensor([1, 589])]; + tensor var_202_end_mask_0 = const()[name = tensor("op_202_end_mask_0"), val = tensor([false, true])]; + tensor var_202_squeeze_mask_0 = const()[name = tensor("op_202_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_202_cast_fp16 = slice_by_index(begin = var_202_begin_0, end = var_202_end_0, end_mask = var_202_end_mask_0, squeeze_mask = var_202_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_202_cast_fp16")]; + tensor slice_by_index_0 = const()[name = tensor("slice_by_index_0"), val = tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588])]; + tensor scatter_0_mode_0 = const()[name = tensor("scatter_0_mode_0"), val = tensor("update")]; + tensor scatter_0_axis_0 = const()[name = tensor("scatter_0_axis_0"), val = tensor(0)]; + tensor scatter_0_validate_indices_0 = const()[name = tensor("scatter_0_validate_indices_0"), val = tensor(false)]; + tensor _aggregated_voice_activity_to_fp16 = const()[name = tensor("_aggregated_voice_activity_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1516096)))]; + tensor scatter_0_cast_fp16 = scatter(axis = scatter_0_axis_0, data = _aggregated_voice_activity_to_fp16, indices = slice_by_index_0, mode = scatter_0_mode_0, updates = var_202_cast_fp16, validate_indices = scatter_0_validate_indices_0)[name = tensor("scatter_0_cast_fp16")]; + tensor scatter_1_mode_0 = const()[name = tensor("scatter_1_mode_0"), val = tensor("update")]; + tensor scatter_1_axis_0 = const()[name = tensor("scatter_1_axis_0"), val = tensor(0)]; + tensor scatter_1_validate_indices_0 = const()[name = tensor("scatter_1_validate_indices_0"), val = tensor(false)]; + tensor var_217_to_fp16 = const()[name = tensor("op_217_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1519744)))]; + tensor scatter_1_cast_fp16 = scatter(axis = scatter_1_axis_0, data = _aggregated_voice_activity_to_fp16, indices = slice_by_index_0, mode = scatter_1_mode_0, updates = var_217_to_fp16, validate_indices = scatter_1_validate_indices_0)[name = tensor("scatter_1_cast_fp16")]; + tensor var_234_begin_0 = const()[name = tensor("op_234_begin_0"), val = tensor([58])]; + tensor var_234_end_0 = const()[name = tensor("op_234_end_0"), val = tensor([647])]; + tensor var_234_end_mask_0 = const()[name = tensor("op_234_end_mask_0"), val = tensor([false])]; + tensor var_234_cast_fp16 = slice_by_index(begin = var_234_begin_0, end = var_234_end_0, end_mask = var_234_end_mask_0, x = scatter_0_cast_fp16)[name = tensor("op_234_cast_fp16")]; + tensor var_237_begin_0 = const()[name = tensor("op_237_begin_0"), val = tensor([1, 0])]; + tensor var_237_end_0 = const()[name = tensor("op_237_end_0"), val = tensor([2, 589])]; + tensor var_237_end_mask_0 = const()[name = tensor("op_237_end_mask_0"), val = tensor([false, true])]; + tensor var_237_squeeze_mask_0 = const()[name = tensor("op_237_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_237_cast_fp16 = slice_by_index(begin = var_237_begin_0, end = var_237_end_0, end_mask = var_237_end_mask_0, squeeze_mask = var_237_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_237_cast_fp16")]; + tensor var_239_cast_fp16 = add(x = var_234_cast_fp16, y = var_237_cast_fp16)[name = tensor("op_239_cast_fp16")]; + tensor slice_by_index_2 = const()[name = tensor("slice_by_index_2"), val = tensor([58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646])]; + tensor scatter_2_mode_0 = const()[name = tensor("scatter_2_mode_0"), val = tensor("update")]; + tensor scatter_2_axis_0 = const()[name = tensor("scatter_2_axis_0"), val = tensor(0)]; + tensor scatter_2_validate_indices_0 = const()[name = tensor("scatter_2_validate_indices_0"), val = tensor(false)]; + tensor scatter_2_cast_fp16 = scatter(axis = scatter_2_axis_0, data = scatter_0_cast_fp16, indices = slice_by_index_2, mode = scatter_2_mode_0, updates = var_239_cast_fp16, validate_indices = scatter_2_validate_indices_0)[name = tensor("scatter_2_cast_fp16")]; + tensor var_249_begin_0 = const()[name = tensor("op_249_begin_0"), val = tensor([58])]; + tensor var_249_end_0 = const()[name = tensor("op_249_end_0"), val = tensor([647])]; + tensor var_249_end_mask_0 = const()[name = tensor("op_249_end_mask_0"), val = tensor([false])]; + tensor var_249_cast_fp16 = slice_by_index(begin = var_249_begin_0, end = var_249_end_0, end_mask = var_249_end_mask_0, x = scatter_1_cast_fp16)[name = tensor("op_249_cast_fp16")]; + tensor var_250_to_fp16 = const()[name = tensor("op_250_to_fp16"), val = tensor(0x1p+0)]; + tensor var_252_cast_fp16 = add(x = var_249_cast_fp16, y = var_250_to_fp16)[name = tensor("op_252_cast_fp16")]; + tensor scatter_3_mode_0 = const()[name = tensor("scatter_3_mode_0"), val = tensor("update")]; + tensor scatter_3_axis_0 = const()[name = tensor("scatter_3_axis_0"), val = tensor(0)]; + tensor scatter_3_validate_indices_0 = const()[name = tensor("scatter_3_validate_indices_0"), val = tensor(false)]; + tensor scatter_3_cast_fp16 = scatter(axis = scatter_3_axis_0, data = scatter_1_cast_fp16, indices = slice_by_index_2, mode = scatter_3_mode_0, updates = var_252_cast_fp16, validate_indices = scatter_3_validate_indices_0)[name = tensor("scatter_3_cast_fp16")]; + tensor var_269_begin_0 = const()[name = tensor("op_269_begin_0"), val = tensor([117])]; + tensor var_269_end_0 = const()[name = tensor("op_269_end_0"), val = tensor([706])]; + tensor var_269_end_mask_0 = const()[name = tensor("op_269_end_mask_0"), val = tensor([false])]; + tensor var_269_cast_fp16 = slice_by_index(begin = var_269_begin_0, end = var_269_end_0, end_mask = var_269_end_mask_0, x = scatter_2_cast_fp16)[name = tensor("op_269_cast_fp16")]; + tensor var_272_begin_0 = const()[name = tensor("op_272_begin_0"), val = tensor([2, 0])]; + tensor var_272_end_0 = const()[name = tensor("op_272_end_0"), val = tensor([3, 589])]; + tensor var_272_end_mask_0 = const()[name = tensor("op_272_end_mask_0"), val = tensor([false, true])]; + tensor var_272_squeeze_mask_0 = const()[name = tensor("op_272_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_272_cast_fp16 = slice_by_index(begin = var_272_begin_0, end = var_272_end_0, end_mask = var_272_end_mask_0, squeeze_mask = var_272_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_272_cast_fp16")]; + tensor var_274_cast_fp16 = add(x = var_269_cast_fp16, y = var_272_cast_fp16)[name = tensor("op_274_cast_fp16")]; + tensor slice_by_index_4 = const()[name = tensor("slice_by_index_4"), val = tensor([117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705])]; + tensor scatter_4_mode_0 = const()[name = tensor("scatter_4_mode_0"), val = tensor("update")]; + tensor scatter_4_axis_0 = const()[name = tensor("scatter_4_axis_0"), val = tensor(0)]; + tensor scatter_4_validate_indices_0 = const()[name = tensor("scatter_4_validate_indices_0"), val = tensor(false)]; + tensor scatter_4_cast_fp16 = scatter(axis = scatter_4_axis_0, data = scatter_2_cast_fp16, indices = slice_by_index_4, mode = scatter_4_mode_0, updates = var_274_cast_fp16, validate_indices = scatter_4_validate_indices_0)[name = tensor("scatter_4_cast_fp16")]; + tensor var_284_begin_0 = const()[name = tensor("op_284_begin_0"), val = tensor([117])]; + tensor var_284_end_0 = const()[name = tensor("op_284_end_0"), val = tensor([706])]; + tensor var_284_end_mask_0 = const()[name = tensor("op_284_end_mask_0"), val = tensor([false])]; + tensor var_284_cast_fp16 = slice_by_index(begin = var_284_begin_0, end = var_284_end_0, end_mask = var_284_end_mask_0, x = scatter_3_cast_fp16)[name = tensor("op_284_cast_fp16")]; + tensor var_285_to_fp16 = const()[name = tensor("op_285_to_fp16"), val = tensor(0x1p+0)]; + tensor var_287_cast_fp16 = add(x = var_284_cast_fp16, y = var_285_to_fp16)[name = tensor("op_287_cast_fp16")]; + tensor scatter_5_mode_0 = const()[name = tensor("scatter_5_mode_0"), val = tensor("update")]; + tensor scatter_5_axis_0 = const()[name = tensor("scatter_5_axis_0"), val = tensor(0)]; + tensor scatter_5_validate_indices_0 = const()[name = tensor("scatter_5_validate_indices_0"), val = tensor(false)]; + tensor scatter_5_cast_fp16 = scatter(axis = scatter_5_axis_0, data = scatter_3_cast_fp16, indices = slice_by_index_4, mode = scatter_5_mode_0, updates = var_287_cast_fp16, validate_indices = scatter_5_validate_indices_0)[name = tensor("scatter_5_cast_fp16")]; + tensor var_304_begin_0 = const()[name = tensor("op_304_begin_0"), val = tensor([176])]; + tensor var_304_end_0 = const()[name = tensor("op_304_end_0"), val = tensor([765])]; + tensor var_304_end_mask_0 = const()[name = tensor("op_304_end_mask_0"), val = tensor([false])]; + tensor var_304_cast_fp16 = slice_by_index(begin = var_304_begin_0, end = var_304_end_0, end_mask = var_304_end_mask_0, x = scatter_4_cast_fp16)[name = tensor("op_304_cast_fp16")]; + tensor var_307_begin_0 = const()[name = tensor("op_307_begin_0"), val = tensor([3, 0])]; + tensor var_307_end_0 = const()[name = tensor("op_307_end_0"), val = tensor([4, 589])]; + tensor var_307_end_mask_0 = const()[name = tensor("op_307_end_mask_0"), val = tensor([false, true])]; + tensor var_307_squeeze_mask_0 = const()[name = tensor("op_307_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_307_cast_fp16 = slice_by_index(begin = var_307_begin_0, end = var_307_end_0, end_mask = var_307_end_mask_0, squeeze_mask = var_307_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_307_cast_fp16")]; + tensor var_309_cast_fp16 = add(x = var_304_cast_fp16, y = var_307_cast_fp16)[name = tensor("op_309_cast_fp16")]; + tensor slice_by_index_6 = const()[name = tensor("slice_by_index_6"), val = tensor([176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764])]; + tensor scatter_6_mode_0 = const()[name = tensor("scatter_6_mode_0"), val = tensor("update")]; + tensor scatter_6_axis_0 = const()[name = tensor("scatter_6_axis_0"), val = tensor(0)]; + tensor scatter_6_validate_indices_0 = const()[name = tensor("scatter_6_validate_indices_0"), val = tensor(false)]; + tensor scatter_6_cast_fp16 = scatter(axis = scatter_6_axis_0, data = scatter_4_cast_fp16, indices = slice_by_index_6, mode = scatter_6_mode_0, updates = var_309_cast_fp16, validate_indices = scatter_6_validate_indices_0)[name = tensor("scatter_6_cast_fp16")]; + tensor var_319_begin_0 = const()[name = tensor("op_319_begin_0"), val = tensor([176])]; + tensor var_319_end_0 = const()[name = tensor("op_319_end_0"), val = tensor([765])]; + tensor var_319_end_mask_0 = const()[name = tensor("op_319_end_mask_0"), val = tensor([false])]; + tensor var_319_cast_fp16 = slice_by_index(begin = var_319_begin_0, end = var_319_end_0, end_mask = var_319_end_mask_0, x = scatter_5_cast_fp16)[name = tensor("op_319_cast_fp16")]; + tensor var_320_to_fp16 = const()[name = tensor("op_320_to_fp16"), val = tensor(0x1p+0)]; + tensor var_322_cast_fp16 = add(x = var_319_cast_fp16, y = var_320_to_fp16)[name = tensor("op_322_cast_fp16")]; + tensor scatter_7_mode_0 = const()[name = tensor("scatter_7_mode_0"), val = tensor("update")]; + tensor scatter_7_axis_0 = const()[name = tensor("scatter_7_axis_0"), val = tensor(0)]; + tensor scatter_7_validate_indices_0 = const()[name = tensor("scatter_7_validate_indices_0"), val = tensor(false)]; + tensor scatter_7_cast_fp16 = scatter(axis = scatter_7_axis_0, data = scatter_5_cast_fp16, indices = slice_by_index_6, mode = scatter_7_mode_0, updates = var_322_cast_fp16, validate_indices = scatter_7_validate_indices_0)[name = tensor("scatter_7_cast_fp16")]; + tensor var_339_begin_0 = const()[name = tensor("op_339_begin_0"), val = tensor([235])]; + tensor var_339_end_0 = const()[name = tensor("op_339_end_0"), val = tensor([824])]; + tensor var_339_end_mask_0 = const()[name = tensor("op_339_end_mask_0"), val = tensor([false])]; + tensor var_339_cast_fp16 = slice_by_index(begin = var_339_begin_0, end = var_339_end_0, end_mask = var_339_end_mask_0, x = scatter_6_cast_fp16)[name = tensor("op_339_cast_fp16")]; + tensor var_342_begin_0 = const()[name = tensor("op_342_begin_0"), val = tensor([4, 0])]; + tensor var_342_end_0 = const()[name = tensor("op_342_end_0"), val = tensor([5, 589])]; + tensor var_342_end_mask_0 = const()[name = tensor("op_342_end_mask_0"), val = tensor([false, true])]; + tensor var_342_squeeze_mask_0 = const()[name = tensor("op_342_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_342_cast_fp16 = slice_by_index(begin = var_342_begin_0, end = var_342_end_0, end_mask = var_342_end_mask_0, squeeze_mask = var_342_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_342_cast_fp16")]; + tensor var_344_cast_fp16 = add(x = var_339_cast_fp16, y = var_342_cast_fp16)[name = tensor("op_344_cast_fp16")]; + tensor slice_by_index_8 = const()[name = tensor("slice_by_index_8"), val = tensor([235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823])]; + tensor scatter_8_mode_0 = const()[name = tensor("scatter_8_mode_0"), val = tensor("update")]; + tensor scatter_8_axis_0 = const()[name = tensor("scatter_8_axis_0"), val = tensor(0)]; + tensor scatter_8_validate_indices_0 = const()[name = tensor("scatter_8_validate_indices_0"), val = tensor(false)]; + tensor scatter_8_cast_fp16 = scatter(axis = scatter_8_axis_0, data = scatter_6_cast_fp16, indices = slice_by_index_8, mode = scatter_8_mode_0, updates = var_344_cast_fp16, validate_indices = scatter_8_validate_indices_0)[name = tensor("scatter_8_cast_fp16")]; + tensor var_354_begin_0 = const()[name = tensor("op_354_begin_0"), val = tensor([235])]; + tensor var_354_end_0 = const()[name = tensor("op_354_end_0"), val = tensor([824])]; + tensor var_354_end_mask_0 = const()[name = tensor("op_354_end_mask_0"), val = tensor([false])]; + tensor var_354_cast_fp16 = slice_by_index(begin = var_354_begin_0, end = var_354_end_0, end_mask = var_354_end_mask_0, x = scatter_7_cast_fp16)[name = tensor("op_354_cast_fp16")]; + tensor var_355_to_fp16 = const()[name = tensor("op_355_to_fp16"), val = tensor(0x1p+0)]; + tensor var_357_cast_fp16 = add(x = var_354_cast_fp16, y = var_355_to_fp16)[name = tensor("op_357_cast_fp16")]; + tensor scatter_9_mode_0 = const()[name = tensor("scatter_9_mode_0"), val = tensor("update")]; + tensor scatter_9_axis_0 = const()[name = tensor("scatter_9_axis_0"), val = tensor(0)]; + tensor scatter_9_validate_indices_0 = const()[name = tensor("scatter_9_validate_indices_0"), val = tensor(false)]; + tensor scatter_9_cast_fp16 = scatter(axis = scatter_9_axis_0, data = scatter_7_cast_fp16, indices = slice_by_index_8, mode = scatter_9_mode_0, updates = var_357_cast_fp16, validate_indices = scatter_9_validate_indices_0)[name = tensor("scatter_9_cast_fp16")]; + tensor var_374_begin_0 = const()[name = tensor("op_374_begin_0"), val = tensor([294])]; + tensor var_374_end_0 = const()[name = tensor("op_374_end_0"), val = tensor([883])]; + tensor var_374_end_mask_0 = const()[name = tensor("op_374_end_mask_0"), val = tensor([false])]; + tensor var_374_cast_fp16 = slice_by_index(begin = var_374_begin_0, end = var_374_end_0, end_mask = var_374_end_mask_0, x = scatter_8_cast_fp16)[name = tensor("op_374_cast_fp16")]; + tensor var_377_begin_0 = const()[name = tensor("op_377_begin_0"), val = tensor([5, 0])]; + tensor var_377_end_0 = const()[name = tensor("op_377_end_0"), val = tensor([6, 589])]; + tensor var_377_end_mask_0 = const()[name = tensor("op_377_end_mask_0"), val = tensor([false, true])]; + tensor var_377_squeeze_mask_0 = const()[name = tensor("op_377_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_377_cast_fp16 = slice_by_index(begin = var_377_begin_0, end = var_377_end_0, end_mask = var_377_end_mask_0, squeeze_mask = var_377_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_377_cast_fp16")]; + tensor var_379_cast_fp16 = add(x = var_374_cast_fp16, y = var_377_cast_fp16)[name = tensor("op_379_cast_fp16")]; + tensor slice_by_index_10 = const()[name = tensor("slice_by_index_10"), val = tensor([294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882])]; + tensor scatter_10_mode_0 = const()[name = tensor("scatter_10_mode_0"), val = tensor("update")]; + tensor scatter_10_axis_0 = const()[name = tensor("scatter_10_axis_0"), val = tensor(0)]; + tensor scatter_10_validate_indices_0 = const()[name = tensor("scatter_10_validate_indices_0"), val = tensor(false)]; + tensor scatter_10_cast_fp16 = scatter(axis = scatter_10_axis_0, data = scatter_8_cast_fp16, indices = slice_by_index_10, mode = scatter_10_mode_0, updates = var_379_cast_fp16, validate_indices = scatter_10_validate_indices_0)[name = tensor("scatter_10_cast_fp16")]; + tensor var_389_begin_0 = const()[name = tensor("op_389_begin_0"), val = tensor([294])]; + tensor var_389_end_0 = const()[name = tensor("op_389_end_0"), val = tensor([883])]; + tensor var_389_end_mask_0 = const()[name = tensor("op_389_end_mask_0"), val = tensor([false])]; + tensor var_389_cast_fp16 = slice_by_index(begin = var_389_begin_0, end = var_389_end_0, end_mask = var_389_end_mask_0, x = scatter_9_cast_fp16)[name = tensor("op_389_cast_fp16")]; + tensor var_390_to_fp16 = const()[name = tensor("op_390_to_fp16"), val = tensor(0x1p+0)]; + tensor var_392_cast_fp16 = add(x = var_389_cast_fp16, y = var_390_to_fp16)[name = tensor("op_392_cast_fp16")]; + tensor scatter_11_mode_0 = const()[name = tensor("scatter_11_mode_0"), val = tensor("update")]; + tensor scatter_11_axis_0 = const()[name = tensor("scatter_11_axis_0"), val = tensor(0)]; + tensor scatter_11_validate_indices_0 = const()[name = tensor("scatter_11_validate_indices_0"), val = tensor(false)]; + tensor scatter_11_cast_fp16 = scatter(axis = scatter_11_axis_0, data = scatter_9_cast_fp16, indices = slice_by_index_10, mode = scatter_11_mode_0, updates = var_392_cast_fp16, validate_indices = scatter_11_validate_indices_0)[name = tensor("scatter_11_cast_fp16")]; + tensor var_409_begin_0 = const()[name = tensor("op_409_begin_0"), val = tensor([353])]; + tensor var_409_end_0 = const()[name = tensor("op_409_end_0"), val = tensor([942])]; + tensor var_409_end_mask_0 = const()[name = tensor("op_409_end_mask_0"), val = tensor([false])]; + tensor var_409_cast_fp16 = slice_by_index(begin = var_409_begin_0, end = var_409_end_0, end_mask = var_409_end_mask_0, x = scatter_10_cast_fp16)[name = tensor("op_409_cast_fp16")]; + tensor var_412_begin_0 = const()[name = tensor("op_412_begin_0"), val = tensor([6, 0])]; + tensor var_412_end_0 = const()[name = tensor("op_412_end_0"), val = tensor([7, 589])]; + tensor var_412_end_mask_0 = const()[name = tensor("op_412_end_mask_0"), val = tensor([false, true])]; + tensor var_412_squeeze_mask_0 = const()[name = tensor("op_412_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_412_cast_fp16 = slice_by_index(begin = var_412_begin_0, end = var_412_end_0, end_mask = var_412_end_mask_0, squeeze_mask = var_412_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_412_cast_fp16")]; + tensor var_414_cast_fp16 = add(x = var_409_cast_fp16, y = var_412_cast_fp16)[name = tensor("op_414_cast_fp16")]; + tensor slice_by_index_12 = const()[name = tensor("slice_by_index_12"), val = tensor([353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941])]; + tensor scatter_12_mode_0 = const()[name = tensor("scatter_12_mode_0"), val = tensor("update")]; + tensor scatter_12_axis_0 = const()[name = tensor("scatter_12_axis_0"), val = tensor(0)]; + tensor scatter_12_validate_indices_0 = const()[name = tensor("scatter_12_validate_indices_0"), val = tensor(false)]; + tensor scatter_12_cast_fp16 = scatter(axis = scatter_12_axis_0, data = scatter_10_cast_fp16, indices = slice_by_index_12, mode = scatter_12_mode_0, updates = var_414_cast_fp16, validate_indices = scatter_12_validate_indices_0)[name = tensor("scatter_12_cast_fp16")]; + tensor var_424_begin_0 = const()[name = tensor("op_424_begin_0"), val = tensor([353])]; + tensor var_424_end_0 = const()[name = tensor("op_424_end_0"), val = tensor([942])]; + tensor var_424_end_mask_0 = const()[name = tensor("op_424_end_mask_0"), val = tensor([false])]; + tensor var_424_cast_fp16 = slice_by_index(begin = var_424_begin_0, end = var_424_end_0, end_mask = var_424_end_mask_0, x = scatter_11_cast_fp16)[name = tensor("op_424_cast_fp16")]; + tensor var_425_to_fp16 = const()[name = tensor("op_425_to_fp16"), val = tensor(0x1p+0)]; + tensor var_427_cast_fp16 = add(x = var_424_cast_fp16, y = var_425_to_fp16)[name = tensor("op_427_cast_fp16")]; + tensor scatter_13_mode_0 = const()[name = tensor("scatter_13_mode_0"), val = tensor("update")]; + tensor scatter_13_axis_0 = const()[name = tensor("scatter_13_axis_0"), val = tensor(0)]; + tensor scatter_13_validate_indices_0 = const()[name = tensor("scatter_13_validate_indices_0"), val = tensor(false)]; + tensor scatter_13_cast_fp16 = scatter(axis = scatter_13_axis_0, data = scatter_11_cast_fp16, indices = slice_by_index_12, mode = scatter_13_mode_0, updates = var_427_cast_fp16, validate_indices = scatter_13_validate_indices_0)[name = tensor("scatter_13_cast_fp16")]; + tensor var_444_begin_0 = const()[name = tensor("op_444_begin_0"), val = tensor([412])]; + tensor var_444_end_0 = const()[name = tensor("op_444_end_0"), val = tensor([1001])]; + tensor var_444_end_mask_0 = const()[name = tensor("op_444_end_mask_0"), val = tensor([false])]; + tensor var_444_cast_fp16 = slice_by_index(begin = var_444_begin_0, end = var_444_end_0, end_mask = var_444_end_mask_0, x = scatter_12_cast_fp16)[name = tensor("op_444_cast_fp16")]; + tensor var_447_begin_0 = const()[name = tensor("op_447_begin_0"), val = tensor([7, 0])]; + tensor var_447_end_0 = const()[name = tensor("op_447_end_0"), val = tensor([8, 589])]; + tensor var_447_end_mask_0 = const()[name = tensor("op_447_end_mask_0"), val = tensor([false, true])]; + tensor var_447_squeeze_mask_0 = const()[name = tensor("op_447_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_447_cast_fp16 = slice_by_index(begin = var_447_begin_0, end = var_447_end_0, end_mask = var_447_end_mask_0, squeeze_mask = var_447_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_447_cast_fp16")]; + tensor var_449_cast_fp16 = add(x = var_444_cast_fp16, y = var_447_cast_fp16)[name = tensor("op_449_cast_fp16")]; + tensor slice_by_index_14 = const()[name = tensor("slice_by_index_14"), val = tensor([412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000])]; + tensor scatter_14_mode_0 = const()[name = tensor("scatter_14_mode_0"), val = tensor("update")]; + tensor scatter_14_axis_0 = const()[name = tensor("scatter_14_axis_0"), val = tensor(0)]; + tensor scatter_14_validate_indices_0 = const()[name = tensor("scatter_14_validate_indices_0"), val = tensor(false)]; + tensor scatter_14_cast_fp16 = scatter(axis = scatter_14_axis_0, data = scatter_12_cast_fp16, indices = slice_by_index_14, mode = scatter_14_mode_0, updates = var_449_cast_fp16, validate_indices = scatter_14_validate_indices_0)[name = tensor("scatter_14_cast_fp16")]; + tensor var_459_begin_0 = const()[name = tensor("op_459_begin_0"), val = tensor([412])]; + tensor var_459_end_0 = const()[name = tensor("op_459_end_0"), val = tensor([1001])]; + tensor var_459_end_mask_0 = const()[name = tensor("op_459_end_mask_0"), val = tensor([false])]; + tensor var_459_cast_fp16 = slice_by_index(begin = var_459_begin_0, end = var_459_end_0, end_mask = var_459_end_mask_0, x = scatter_13_cast_fp16)[name = tensor("op_459_cast_fp16")]; + tensor var_460_to_fp16 = const()[name = tensor("op_460_to_fp16"), val = tensor(0x1p+0)]; + tensor var_462_cast_fp16 = add(x = var_459_cast_fp16, y = var_460_to_fp16)[name = tensor("op_462_cast_fp16")]; + tensor scatter_15_mode_0 = const()[name = tensor("scatter_15_mode_0"), val = tensor("update")]; + tensor scatter_15_axis_0 = const()[name = tensor("scatter_15_axis_0"), val = tensor(0)]; + tensor scatter_15_validate_indices_0 = const()[name = tensor("scatter_15_validate_indices_0"), val = tensor(false)]; + tensor scatter_15_cast_fp16 = scatter(axis = scatter_15_axis_0, data = scatter_13_cast_fp16, indices = slice_by_index_14, mode = scatter_15_mode_0, updates = var_462_cast_fp16, validate_indices = scatter_15_validate_indices_0)[name = tensor("scatter_15_cast_fp16")]; + tensor var_479_begin_0 = const()[name = tensor("op_479_begin_0"), val = tensor([471])]; + tensor var_479_end_0 = const()[name = tensor("op_479_end_0"), val = tensor([1060])]; + tensor var_479_end_mask_0 = const()[name = tensor("op_479_end_mask_0"), val = tensor([false])]; + tensor var_479_cast_fp16 = slice_by_index(begin = var_479_begin_0, end = var_479_end_0, end_mask = var_479_end_mask_0, x = scatter_14_cast_fp16)[name = tensor("op_479_cast_fp16")]; + tensor var_482_begin_0 = const()[name = tensor("op_482_begin_0"), val = tensor([8, 0])]; + tensor var_482_end_0 = const()[name = tensor("op_482_end_0"), val = tensor([9, 589])]; + tensor var_482_end_mask_0 = const()[name = tensor("op_482_end_mask_0"), val = tensor([false, true])]; + tensor var_482_squeeze_mask_0 = const()[name = tensor("op_482_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_482_cast_fp16 = slice_by_index(begin = var_482_begin_0, end = var_482_end_0, end_mask = var_482_end_mask_0, squeeze_mask = var_482_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_482_cast_fp16")]; + tensor var_484_cast_fp16 = add(x = var_479_cast_fp16, y = var_482_cast_fp16)[name = tensor("op_484_cast_fp16")]; + tensor slice_by_index_16 = const()[name = tensor("slice_by_index_16"), val = tensor([471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059])]; + tensor scatter_16_mode_0 = const()[name = tensor("scatter_16_mode_0"), val = tensor("update")]; + tensor scatter_16_axis_0 = const()[name = tensor("scatter_16_axis_0"), val = tensor(0)]; + tensor scatter_16_validate_indices_0 = const()[name = tensor("scatter_16_validate_indices_0"), val = tensor(false)]; + tensor scatter_16_cast_fp16 = scatter(axis = scatter_16_axis_0, data = scatter_14_cast_fp16, indices = slice_by_index_16, mode = scatter_16_mode_0, updates = var_484_cast_fp16, validate_indices = scatter_16_validate_indices_0)[name = tensor("scatter_16_cast_fp16")]; + tensor var_494_begin_0 = const()[name = tensor("op_494_begin_0"), val = tensor([471])]; + tensor var_494_end_0 = const()[name = tensor("op_494_end_0"), val = tensor([1060])]; + tensor var_494_end_mask_0 = const()[name = tensor("op_494_end_mask_0"), val = tensor([false])]; + tensor var_494_cast_fp16 = slice_by_index(begin = var_494_begin_0, end = var_494_end_0, end_mask = var_494_end_mask_0, x = scatter_15_cast_fp16)[name = tensor("op_494_cast_fp16")]; + tensor var_495_to_fp16 = const()[name = tensor("op_495_to_fp16"), val = tensor(0x1p+0)]; + tensor var_497_cast_fp16 = add(x = var_494_cast_fp16, y = var_495_to_fp16)[name = tensor("op_497_cast_fp16")]; + tensor scatter_17_mode_0 = const()[name = tensor("scatter_17_mode_0"), val = tensor("update")]; + tensor scatter_17_axis_0 = const()[name = tensor("scatter_17_axis_0"), val = tensor(0)]; + tensor scatter_17_validate_indices_0 = const()[name = tensor("scatter_17_validate_indices_0"), val = tensor(false)]; + tensor scatter_17_cast_fp16 = scatter(axis = scatter_17_axis_0, data = scatter_15_cast_fp16, indices = slice_by_index_16, mode = scatter_17_mode_0, updates = var_497_cast_fp16, validate_indices = scatter_17_validate_indices_0)[name = tensor("scatter_17_cast_fp16")]; + tensor var_514_begin_0 = const()[name = tensor("op_514_begin_0"), val = tensor([530])]; + tensor var_514_end_0 = const()[name = tensor("op_514_end_0"), val = tensor([1119])]; + tensor var_514_end_mask_0 = const()[name = tensor("op_514_end_mask_0"), val = tensor([false])]; + tensor var_514_cast_fp16 = slice_by_index(begin = var_514_begin_0, end = var_514_end_0, end_mask = var_514_end_mask_0, x = scatter_16_cast_fp16)[name = tensor("op_514_cast_fp16")]; + tensor var_517_begin_0 = const()[name = tensor("op_517_begin_0"), val = tensor([9, 0])]; + tensor var_517_end_0 = const()[name = tensor("op_517_end_0"), val = tensor([10, 589])]; + tensor var_517_end_mask_0 = const()[name = tensor("op_517_end_mask_0"), val = tensor([false, true])]; + tensor var_517_squeeze_mask_0 = const()[name = tensor("op_517_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_517_cast_fp16 = slice_by_index(begin = var_517_begin_0, end = var_517_end_0, end_mask = var_517_end_mask_0, squeeze_mask = var_517_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_517_cast_fp16")]; + tensor var_519_cast_fp16 = add(x = var_514_cast_fp16, y = var_517_cast_fp16)[name = tensor("op_519_cast_fp16")]; + tensor slice_by_index_18 = const()[name = tensor("slice_by_index_18"), val = tensor([530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118])]; + tensor scatter_18_mode_0 = const()[name = tensor("scatter_18_mode_0"), val = tensor("update")]; + tensor scatter_18_axis_0 = const()[name = tensor("scatter_18_axis_0"), val = tensor(0)]; + tensor scatter_18_validate_indices_0 = const()[name = tensor("scatter_18_validate_indices_0"), val = tensor(false)]; + tensor scatter_18_cast_fp16 = scatter(axis = scatter_18_axis_0, data = scatter_16_cast_fp16, indices = slice_by_index_18, mode = scatter_18_mode_0, updates = var_519_cast_fp16, validate_indices = scatter_18_validate_indices_0)[name = tensor("scatter_18_cast_fp16")]; + tensor var_529_begin_0 = const()[name = tensor("op_529_begin_0"), val = tensor([530])]; + tensor var_529_end_0 = const()[name = tensor("op_529_end_0"), val = tensor([1119])]; + tensor var_529_end_mask_0 = const()[name = tensor("op_529_end_mask_0"), val = tensor([false])]; + tensor var_529_cast_fp16 = slice_by_index(begin = var_529_begin_0, end = var_529_end_0, end_mask = var_529_end_mask_0, x = scatter_17_cast_fp16)[name = tensor("op_529_cast_fp16")]; + tensor var_530_to_fp16 = const()[name = tensor("op_530_to_fp16"), val = tensor(0x1p+0)]; + tensor var_532_cast_fp16 = add(x = var_529_cast_fp16, y = var_530_to_fp16)[name = tensor("op_532_cast_fp16")]; + tensor scatter_19_mode_0 = const()[name = tensor("scatter_19_mode_0"), val = tensor("update")]; + tensor scatter_19_axis_0 = const()[name = tensor("scatter_19_axis_0"), val = tensor(0)]; + tensor scatter_19_validate_indices_0 = const()[name = tensor("scatter_19_validate_indices_0"), val = tensor(false)]; + tensor scatter_19_cast_fp16 = scatter(axis = scatter_19_axis_0, data = scatter_17_cast_fp16, indices = slice_by_index_18, mode = scatter_19_mode_0, updates = var_532_cast_fp16, validate_indices = scatter_19_validate_indices_0)[name = tensor("scatter_19_cast_fp16")]; + tensor var_549_begin_0 = const()[name = tensor("op_549_begin_0"), val = tensor([589])]; + tensor var_549_end_0 = const()[name = tensor("op_549_end_0"), val = tensor([1178])]; + tensor var_549_end_mask_0 = const()[name = tensor("op_549_end_mask_0"), val = tensor([false])]; + tensor var_549_cast_fp16 = slice_by_index(begin = var_549_begin_0, end = var_549_end_0, end_mask = var_549_end_mask_0, x = scatter_18_cast_fp16)[name = tensor("op_549_cast_fp16")]; + tensor var_552_begin_0 = const()[name = tensor("op_552_begin_0"), val = tensor([10, 0])]; + tensor var_552_end_0 = const()[name = tensor("op_552_end_0"), val = tensor([11, 589])]; + tensor var_552_end_mask_0 = const()[name = tensor("op_552_end_mask_0"), val = tensor([false, true])]; + tensor var_552_squeeze_mask_0 = const()[name = tensor("op_552_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_552_cast_fp16 = slice_by_index(begin = var_552_begin_0, end = var_552_end_0, end_mask = var_552_end_mask_0, squeeze_mask = var_552_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_552_cast_fp16")]; + tensor var_554_cast_fp16 = add(x = var_549_cast_fp16, y = var_552_cast_fp16)[name = tensor("op_554_cast_fp16")]; + tensor slice_by_index_20 = const()[name = tensor("slice_by_index_20"), val = tensor([589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177])]; + tensor scatter_20_mode_0 = const()[name = tensor("scatter_20_mode_0"), val = tensor("update")]; + tensor scatter_20_axis_0 = const()[name = tensor("scatter_20_axis_0"), val = tensor(0)]; + tensor scatter_20_validate_indices_0 = const()[name = tensor("scatter_20_validate_indices_0"), val = tensor(false)]; + tensor scatter_20_cast_fp16 = scatter(axis = scatter_20_axis_0, data = scatter_18_cast_fp16, indices = slice_by_index_20, mode = scatter_20_mode_0, updates = var_554_cast_fp16, validate_indices = scatter_20_validate_indices_0)[name = tensor("scatter_20_cast_fp16")]; + tensor var_564_begin_0 = const()[name = tensor("op_564_begin_0"), val = tensor([589])]; + tensor var_564_end_0 = const()[name = tensor("op_564_end_0"), val = tensor([1178])]; + tensor var_564_end_mask_0 = const()[name = tensor("op_564_end_mask_0"), val = tensor([false])]; + tensor var_564_cast_fp16 = slice_by_index(begin = var_564_begin_0, end = var_564_end_0, end_mask = var_564_end_mask_0, x = scatter_19_cast_fp16)[name = tensor("op_564_cast_fp16")]; + tensor var_565_to_fp16 = const()[name = tensor("op_565_to_fp16"), val = tensor(0x1p+0)]; + tensor var_567_cast_fp16 = add(x = var_564_cast_fp16, y = var_565_to_fp16)[name = tensor("op_567_cast_fp16")]; + tensor scatter_21_mode_0 = const()[name = tensor("scatter_21_mode_0"), val = tensor("update")]; + tensor scatter_21_axis_0 = const()[name = tensor("scatter_21_axis_0"), val = tensor(0)]; + tensor scatter_21_validate_indices_0 = const()[name = tensor("scatter_21_validate_indices_0"), val = tensor(false)]; + tensor scatter_21_cast_fp16 = scatter(axis = scatter_21_axis_0, data = scatter_19_cast_fp16, indices = slice_by_index_20, mode = scatter_21_mode_0, updates = var_567_cast_fp16, validate_indices = scatter_21_validate_indices_0)[name = tensor("scatter_21_cast_fp16")]; + tensor var_584_begin_0 = const()[name = tensor("op_584_begin_0"), val = tensor([647])]; + tensor var_584_end_0 = const()[name = tensor("op_584_end_0"), val = tensor([1236])]; + tensor var_584_end_mask_0 = const()[name = tensor("op_584_end_mask_0"), val = tensor([false])]; + tensor var_584_cast_fp16 = slice_by_index(begin = var_584_begin_0, end = var_584_end_0, end_mask = var_584_end_mask_0, x = scatter_20_cast_fp16)[name = tensor("op_584_cast_fp16")]; + tensor var_587_begin_0 = const()[name = tensor("op_587_begin_0"), val = tensor([11, 0])]; + tensor var_587_end_0 = const()[name = tensor("op_587_end_0"), val = tensor([12, 589])]; + tensor var_587_end_mask_0 = const()[name = tensor("op_587_end_mask_0"), val = tensor([false, true])]; + tensor var_587_squeeze_mask_0 = const()[name = tensor("op_587_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_587_cast_fp16 = slice_by_index(begin = var_587_begin_0, end = var_587_end_0, end_mask = var_587_end_mask_0, squeeze_mask = var_587_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_587_cast_fp16")]; + tensor var_589_cast_fp16 = add(x = var_584_cast_fp16, y = var_587_cast_fp16)[name = tensor("op_589_cast_fp16")]; + tensor slice_by_index_22 = const()[name = tensor("slice_by_index_22"), val = tensor([647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235])]; + tensor scatter_22_mode_0 = const()[name = tensor("scatter_22_mode_0"), val = tensor("update")]; + tensor scatter_22_axis_0 = const()[name = tensor("scatter_22_axis_0"), val = tensor(0)]; + tensor scatter_22_validate_indices_0 = const()[name = tensor("scatter_22_validate_indices_0"), val = tensor(false)]; + tensor scatter_22_cast_fp16 = scatter(axis = scatter_22_axis_0, data = scatter_20_cast_fp16, indices = slice_by_index_22, mode = scatter_22_mode_0, updates = var_589_cast_fp16, validate_indices = scatter_22_validate_indices_0)[name = tensor("scatter_22_cast_fp16")]; + tensor var_599_begin_0 = const()[name = tensor("op_599_begin_0"), val = tensor([647])]; + tensor var_599_end_0 = const()[name = tensor("op_599_end_0"), val = tensor([1236])]; + tensor var_599_end_mask_0 = const()[name = tensor("op_599_end_mask_0"), val = tensor([false])]; + tensor var_599_cast_fp16 = slice_by_index(begin = var_599_begin_0, end = var_599_end_0, end_mask = var_599_end_mask_0, x = scatter_21_cast_fp16)[name = tensor("op_599_cast_fp16")]; + tensor var_600_to_fp16 = const()[name = tensor("op_600_to_fp16"), val = tensor(0x1p+0)]; + tensor var_602_cast_fp16 = add(x = var_599_cast_fp16, y = var_600_to_fp16)[name = tensor("op_602_cast_fp16")]; + tensor scatter_23_mode_0 = const()[name = tensor("scatter_23_mode_0"), val = tensor("update")]; + tensor scatter_23_axis_0 = const()[name = tensor("scatter_23_axis_0"), val = tensor(0)]; + tensor scatter_23_validate_indices_0 = const()[name = tensor("scatter_23_validate_indices_0"), val = tensor(false)]; + tensor scatter_23_cast_fp16 = scatter(axis = scatter_23_axis_0, data = scatter_21_cast_fp16, indices = slice_by_index_22, mode = scatter_23_mode_0, updates = var_602_cast_fp16, validate_indices = scatter_23_validate_indices_0)[name = tensor("scatter_23_cast_fp16")]; + tensor var_619_begin_0 = const()[name = tensor("op_619_begin_0"), val = tensor([706])]; + tensor var_619_end_0 = const()[name = tensor("op_619_end_0"), val = tensor([1295])]; + tensor var_619_end_mask_0 = const()[name = tensor("op_619_end_mask_0"), val = tensor([false])]; + tensor var_619_cast_fp16 = slice_by_index(begin = var_619_begin_0, end = var_619_end_0, end_mask = var_619_end_mask_0, x = scatter_22_cast_fp16)[name = tensor("op_619_cast_fp16")]; + tensor var_622_begin_0 = const()[name = tensor("op_622_begin_0"), val = tensor([12, 0])]; + tensor var_622_end_0 = const()[name = tensor("op_622_end_0"), val = tensor([13, 589])]; + tensor var_622_end_mask_0 = const()[name = tensor("op_622_end_mask_0"), val = tensor([false, true])]; + tensor var_622_squeeze_mask_0 = const()[name = tensor("op_622_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_622_cast_fp16 = slice_by_index(begin = var_622_begin_0, end = var_622_end_0, end_mask = var_622_end_mask_0, squeeze_mask = var_622_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_622_cast_fp16")]; + tensor var_624_cast_fp16 = add(x = var_619_cast_fp16, y = var_622_cast_fp16)[name = tensor("op_624_cast_fp16")]; + tensor slice_by_index_24 = const()[name = tensor("slice_by_index_24"), val = tensor([706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294])]; + tensor scatter_24_mode_0 = const()[name = tensor("scatter_24_mode_0"), val = tensor("update")]; + tensor scatter_24_axis_0 = const()[name = tensor("scatter_24_axis_0"), val = tensor(0)]; + tensor scatter_24_validate_indices_0 = const()[name = tensor("scatter_24_validate_indices_0"), val = tensor(false)]; + tensor scatter_24_cast_fp16 = scatter(axis = scatter_24_axis_0, data = scatter_22_cast_fp16, indices = slice_by_index_24, mode = scatter_24_mode_0, updates = var_624_cast_fp16, validate_indices = scatter_24_validate_indices_0)[name = tensor("scatter_24_cast_fp16")]; + tensor var_634_begin_0 = const()[name = tensor("op_634_begin_0"), val = tensor([706])]; + tensor var_634_end_0 = const()[name = tensor("op_634_end_0"), val = tensor([1295])]; + tensor var_634_end_mask_0 = const()[name = tensor("op_634_end_mask_0"), val = tensor([false])]; + tensor var_634_cast_fp16 = slice_by_index(begin = var_634_begin_0, end = var_634_end_0, end_mask = var_634_end_mask_0, x = scatter_23_cast_fp16)[name = tensor("op_634_cast_fp16")]; + tensor var_635_to_fp16 = const()[name = tensor("op_635_to_fp16"), val = tensor(0x1p+0)]; + tensor var_637_cast_fp16 = add(x = var_634_cast_fp16, y = var_635_to_fp16)[name = tensor("op_637_cast_fp16")]; + tensor scatter_25_mode_0 = const()[name = tensor("scatter_25_mode_0"), val = tensor("update")]; + tensor scatter_25_axis_0 = const()[name = tensor("scatter_25_axis_0"), val = tensor(0)]; + tensor scatter_25_validate_indices_0 = const()[name = tensor("scatter_25_validate_indices_0"), val = tensor(false)]; + tensor scatter_25_cast_fp16 = scatter(axis = scatter_25_axis_0, data = scatter_23_cast_fp16, indices = slice_by_index_24, mode = scatter_25_mode_0, updates = var_637_cast_fp16, validate_indices = scatter_25_validate_indices_0)[name = tensor("scatter_25_cast_fp16")]; + tensor var_654_begin_0 = const()[name = tensor("op_654_begin_0"), val = tensor([765])]; + tensor var_654_end_0 = const()[name = tensor("op_654_end_0"), val = tensor([1354])]; + tensor var_654_end_mask_0 = const()[name = tensor("op_654_end_mask_0"), val = tensor([false])]; + tensor var_654_cast_fp16 = slice_by_index(begin = var_654_begin_0, end = var_654_end_0, end_mask = var_654_end_mask_0, x = scatter_24_cast_fp16)[name = tensor("op_654_cast_fp16")]; + tensor var_657_begin_0 = const()[name = tensor("op_657_begin_0"), val = tensor([13, 0])]; + tensor var_657_end_0 = const()[name = tensor("op_657_end_0"), val = tensor([14, 589])]; + tensor var_657_end_mask_0 = const()[name = tensor("op_657_end_mask_0"), val = tensor([false, true])]; + tensor var_657_squeeze_mask_0 = const()[name = tensor("op_657_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_657_cast_fp16 = slice_by_index(begin = var_657_begin_0, end = var_657_end_0, end_mask = var_657_end_mask_0, squeeze_mask = var_657_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_657_cast_fp16")]; + tensor var_659_cast_fp16 = add(x = var_654_cast_fp16, y = var_657_cast_fp16)[name = tensor("op_659_cast_fp16")]; + tensor slice_by_index_26 = const()[name = tensor("slice_by_index_26"), val = tensor([765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353])]; + tensor scatter_26_mode_0 = const()[name = tensor("scatter_26_mode_0"), val = tensor("update")]; + tensor scatter_26_axis_0 = const()[name = tensor("scatter_26_axis_0"), val = tensor(0)]; + tensor scatter_26_validate_indices_0 = const()[name = tensor("scatter_26_validate_indices_0"), val = tensor(false)]; + tensor scatter_26_cast_fp16 = scatter(axis = scatter_26_axis_0, data = scatter_24_cast_fp16, indices = slice_by_index_26, mode = scatter_26_mode_0, updates = var_659_cast_fp16, validate_indices = scatter_26_validate_indices_0)[name = tensor("scatter_26_cast_fp16")]; + tensor var_669_begin_0 = const()[name = tensor("op_669_begin_0"), val = tensor([765])]; + tensor var_669_end_0 = const()[name = tensor("op_669_end_0"), val = tensor([1354])]; + tensor var_669_end_mask_0 = const()[name = tensor("op_669_end_mask_0"), val = tensor([false])]; + tensor var_669_cast_fp16 = slice_by_index(begin = var_669_begin_0, end = var_669_end_0, end_mask = var_669_end_mask_0, x = scatter_25_cast_fp16)[name = tensor("op_669_cast_fp16")]; + tensor var_670_to_fp16 = const()[name = tensor("op_670_to_fp16"), val = tensor(0x1p+0)]; + tensor var_672_cast_fp16 = add(x = var_669_cast_fp16, y = var_670_to_fp16)[name = tensor("op_672_cast_fp16")]; + tensor scatter_27_mode_0 = const()[name = tensor("scatter_27_mode_0"), val = tensor("update")]; + tensor scatter_27_axis_0 = const()[name = tensor("scatter_27_axis_0"), val = tensor(0)]; + tensor scatter_27_validate_indices_0 = const()[name = tensor("scatter_27_validate_indices_0"), val = tensor(false)]; + tensor scatter_27_cast_fp16 = scatter(axis = scatter_27_axis_0, data = scatter_25_cast_fp16, indices = slice_by_index_26, mode = scatter_27_mode_0, updates = var_672_cast_fp16, validate_indices = scatter_27_validate_indices_0)[name = tensor("scatter_27_cast_fp16")]; + tensor var_689_begin_0 = const()[name = tensor("op_689_begin_0"), val = tensor([824])]; + tensor var_689_end_0 = const()[name = tensor("op_689_end_0"), val = tensor([1413])]; + tensor var_689_end_mask_0 = const()[name = tensor("op_689_end_mask_0"), val = tensor([false])]; + tensor var_689_cast_fp16 = slice_by_index(begin = var_689_begin_0, end = var_689_end_0, end_mask = var_689_end_mask_0, x = scatter_26_cast_fp16)[name = tensor("op_689_cast_fp16")]; + tensor var_692_begin_0 = const()[name = tensor("op_692_begin_0"), val = tensor([14, 0])]; + tensor var_692_end_0 = const()[name = tensor("op_692_end_0"), val = tensor([15, 589])]; + tensor var_692_end_mask_0 = const()[name = tensor("op_692_end_mask_0"), val = tensor([false, true])]; + tensor var_692_squeeze_mask_0 = const()[name = tensor("op_692_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_692_cast_fp16 = slice_by_index(begin = var_692_begin_0, end = var_692_end_0, end_mask = var_692_end_mask_0, squeeze_mask = var_692_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_692_cast_fp16")]; + tensor var_694_cast_fp16 = add(x = var_689_cast_fp16, y = var_692_cast_fp16)[name = tensor("op_694_cast_fp16")]; + tensor slice_by_index_28 = const()[name = tensor("slice_by_index_28"), val = tensor([824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412])]; + tensor scatter_28_mode_0 = const()[name = tensor("scatter_28_mode_0"), val = tensor("update")]; + tensor scatter_28_axis_0 = const()[name = tensor("scatter_28_axis_0"), val = tensor(0)]; + tensor scatter_28_validate_indices_0 = const()[name = tensor("scatter_28_validate_indices_0"), val = tensor(false)]; + tensor scatter_28_cast_fp16 = scatter(axis = scatter_28_axis_0, data = scatter_26_cast_fp16, indices = slice_by_index_28, mode = scatter_28_mode_0, updates = var_694_cast_fp16, validate_indices = scatter_28_validate_indices_0)[name = tensor("scatter_28_cast_fp16")]; + tensor var_704_begin_0 = const()[name = tensor("op_704_begin_0"), val = tensor([824])]; + tensor var_704_end_0 = const()[name = tensor("op_704_end_0"), val = tensor([1413])]; + tensor var_704_end_mask_0 = const()[name = tensor("op_704_end_mask_0"), val = tensor([false])]; + tensor var_704_cast_fp16 = slice_by_index(begin = var_704_begin_0, end = var_704_end_0, end_mask = var_704_end_mask_0, x = scatter_27_cast_fp16)[name = tensor("op_704_cast_fp16")]; + tensor var_705_to_fp16 = const()[name = tensor("op_705_to_fp16"), val = tensor(0x1p+0)]; + tensor var_707_cast_fp16 = add(x = var_704_cast_fp16, y = var_705_to_fp16)[name = tensor("op_707_cast_fp16")]; + tensor scatter_29_mode_0 = const()[name = tensor("scatter_29_mode_0"), val = tensor("update")]; + tensor scatter_29_axis_0 = const()[name = tensor("scatter_29_axis_0"), val = tensor(0)]; + tensor scatter_29_validate_indices_0 = const()[name = tensor("scatter_29_validate_indices_0"), val = tensor(false)]; + tensor scatter_29_cast_fp16 = scatter(axis = scatter_29_axis_0, data = scatter_27_cast_fp16, indices = slice_by_index_28, mode = scatter_29_mode_0, updates = var_707_cast_fp16, validate_indices = scatter_29_validate_indices_0)[name = tensor("scatter_29_cast_fp16")]; + tensor var_724_begin_0 = const()[name = tensor("op_724_begin_0"), val = tensor([883])]; + tensor var_724_end_0 = const()[name = tensor("op_724_end_0"), val = tensor([1472])]; + tensor var_724_end_mask_0 = const()[name = tensor("op_724_end_mask_0"), val = tensor([false])]; + tensor var_724_cast_fp16 = slice_by_index(begin = var_724_begin_0, end = var_724_end_0, end_mask = var_724_end_mask_0, x = scatter_28_cast_fp16)[name = tensor("op_724_cast_fp16")]; + tensor var_727_begin_0 = const()[name = tensor("op_727_begin_0"), val = tensor([15, 0])]; + tensor var_727_end_0 = const()[name = tensor("op_727_end_0"), val = tensor([16, 589])]; + tensor var_727_end_mask_0 = const()[name = tensor("op_727_end_mask_0"), val = tensor([false, true])]; + tensor var_727_squeeze_mask_0 = const()[name = tensor("op_727_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_727_cast_fp16 = slice_by_index(begin = var_727_begin_0, end = var_727_end_0, end_mask = var_727_end_mask_0, squeeze_mask = var_727_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_727_cast_fp16")]; + tensor var_729_cast_fp16 = add(x = var_724_cast_fp16, y = var_727_cast_fp16)[name = tensor("op_729_cast_fp16")]; + tensor slice_by_index_30 = const()[name = tensor("slice_by_index_30"), val = tensor([883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471])]; + tensor scatter_30_mode_0 = const()[name = tensor("scatter_30_mode_0"), val = tensor("update")]; + tensor scatter_30_axis_0 = const()[name = tensor("scatter_30_axis_0"), val = tensor(0)]; + tensor scatter_30_validate_indices_0 = const()[name = tensor("scatter_30_validate_indices_0"), val = tensor(false)]; + tensor scatter_30_cast_fp16 = scatter(axis = scatter_30_axis_0, data = scatter_28_cast_fp16, indices = slice_by_index_30, mode = scatter_30_mode_0, updates = var_729_cast_fp16, validate_indices = scatter_30_validate_indices_0)[name = tensor("scatter_30_cast_fp16")]; + tensor var_739_begin_0 = const()[name = tensor("op_739_begin_0"), val = tensor([883])]; + tensor var_739_end_0 = const()[name = tensor("op_739_end_0"), val = tensor([1472])]; + tensor var_739_end_mask_0 = const()[name = tensor("op_739_end_mask_0"), val = tensor([false])]; + tensor var_739_cast_fp16 = slice_by_index(begin = var_739_begin_0, end = var_739_end_0, end_mask = var_739_end_mask_0, x = scatter_29_cast_fp16)[name = tensor("op_739_cast_fp16")]; + tensor var_740_to_fp16 = const()[name = tensor("op_740_to_fp16"), val = tensor(0x1p+0)]; + tensor var_742_cast_fp16 = add(x = var_739_cast_fp16, y = var_740_to_fp16)[name = tensor("op_742_cast_fp16")]; + tensor scatter_31_mode_0 = const()[name = tensor("scatter_31_mode_0"), val = tensor("update")]; + tensor scatter_31_axis_0 = const()[name = tensor("scatter_31_axis_0"), val = tensor(0)]; + tensor scatter_31_validate_indices_0 = const()[name = tensor("scatter_31_validate_indices_0"), val = tensor(false)]; + tensor scatter_31_cast_fp16 = scatter(axis = scatter_31_axis_0, data = scatter_29_cast_fp16, indices = slice_by_index_30, mode = scatter_31_mode_0, updates = var_742_cast_fp16, validate_indices = scatter_31_validate_indices_0)[name = tensor("scatter_31_cast_fp16")]; + tensor var_759_begin_0 = const()[name = tensor("op_759_begin_0"), val = tensor([942])]; + tensor var_759_end_0 = const()[name = tensor("op_759_end_0"), val = tensor([1531])]; + tensor var_759_end_mask_0 = const()[name = tensor("op_759_end_mask_0"), val = tensor([false])]; + tensor var_759_cast_fp16 = slice_by_index(begin = var_759_begin_0, end = var_759_end_0, end_mask = var_759_end_mask_0, x = scatter_30_cast_fp16)[name = tensor("op_759_cast_fp16")]; + tensor var_762_begin_0 = const()[name = tensor("op_762_begin_0"), val = tensor([16, 0])]; + tensor var_762_end_0 = const()[name = tensor("op_762_end_0"), val = tensor([17, 589])]; + tensor var_762_end_mask_0 = const()[name = tensor("op_762_end_mask_0"), val = tensor([false, true])]; + tensor var_762_squeeze_mask_0 = const()[name = tensor("op_762_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_762_cast_fp16 = slice_by_index(begin = var_762_begin_0, end = var_762_end_0, end_mask = var_762_end_mask_0, squeeze_mask = var_762_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_762_cast_fp16")]; + tensor var_764_cast_fp16 = add(x = var_759_cast_fp16, y = var_762_cast_fp16)[name = tensor("op_764_cast_fp16")]; + tensor slice_by_index_32 = const()[name = tensor("slice_by_index_32"), val = tensor([942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523, 1524, 1525, 1526, 1527, 1528, 1529, 1530])]; + tensor scatter_32_mode_0 = const()[name = tensor("scatter_32_mode_0"), val = tensor("update")]; + tensor scatter_32_axis_0 = const()[name = tensor("scatter_32_axis_0"), val = tensor(0)]; + tensor scatter_32_validate_indices_0 = const()[name = tensor("scatter_32_validate_indices_0"), val = tensor(false)]; + tensor scatter_32_cast_fp16 = scatter(axis = scatter_32_axis_0, data = scatter_30_cast_fp16, indices = slice_by_index_32, mode = scatter_32_mode_0, updates = var_764_cast_fp16, validate_indices = scatter_32_validate_indices_0)[name = tensor("scatter_32_cast_fp16")]; + tensor var_774_begin_0 = const()[name = tensor("op_774_begin_0"), val = tensor([942])]; + tensor var_774_end_0 = const()[name = tensor("op_774_end_0"), val = tensor([1531])]; + tensor var_774_end_mask_0 = const()[name = tensor("op_774_end_mask_0"), val = tensor([false])]; + tensor var_774_cast_fp16 = slice_by_index(begin = var_774_begin_0, end = var_774_end_0, end_mask = var_774_end_mask_0, x = scatter_31_cast_fp16)[name = tensor("op_774_cast_fp16")]; + tensor var_775_to_fp16 = const()[name = tensor("op_775_to_fp16"), val = tensor(0x1p+0)]; + tensor var_777_cast_fp16 = add(x = var_774_cast_fp16, y = var_775_to_fp16)[name = tensor("op_777_cast_fp16")]; + tensor scatter_33_mode_0 = const()[name = tensor("scatter_33_mode_0"), val = tensor("update")]; + tensor scatter_33_axis_0 = const()[name = tensor("scatter_33_axis_0"), val = tensor(0)]; + tensor scatter_33_validate_indices_0 = const()[name = tensor("scatter_33_validate_indices_0"), val = tensor(false)]; + tensor scatter_33_cast_fp16 = scatter(axis = scatter_33_axis_0, data = scatter_31_cast_fp16, indices = slice_by_index_32, mode = scatter_33_mode_0, updates = var_777_cast_fp16, validate_indices = scatter_33_validate_indices_0)[name = tensor("scatter_33_cast_fp16")]; + tensor var_794_begin_0 = const()[name = tensor("op_794_begin_0"), val = tensor([1001])]; + tensor var_794_end_0 = const()[name = tensor("op_794_end_0"), val = tensor([1590])]; + tensor var_794_end_mask_0 = const()[name = tensor("op_794_end_mask_0"), val = tensor([false])]; + tensor var_794_cast_fp16 = slice_by_index(begin = var_794_begin_0, end = var_794_end_0, end_mask = var_794_end_mask_0, x = scatter_32_cast_fp16)[name = tensor("op_794_cast_fp16")]; + tensor var_797_begin_0 = const()[name = tensor("op_797_begin_0"), val = tensor([17, 0])]; + tensor var_797_end_0 = const()[name = tensor("op_797_end_0"), val = tensor([18, 589])]; + tensor var_797_end_mask_0 = const()[name = tensor("op_797_end_mask_0"), val = tensor([false, true])]; + tensor var_797_squeeze_mask_0 = const()[name = tensor("op_797_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_797_cast_fp16 = slice_by_index(begin = var_797_begin_0, end = var_797_end_0, end_mask = var_797_end_mask_0, squeeze_mask = var_797_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_797_cast_fp16")]; + tensor var_799_cast_fp16 = add(x = var_794_cast_fp16, y = var_797_cast_fp16)[name = tensor("op_799_cast_fp16")]; + tensor slice_by_index_34 = const()[name = tensor("slice_by_index_34"), val = tensor([1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523, 1524, 1525, 1526, 1527, 1528, 1529, 1530, 1531, 1532, 1533, 1534, 1535, 1536, 1537, 1538, 1539, 1540, 1541, 1542, 1543, 1544, 1545, 1546, 1547, 1548, 1549, 1550, 1551, 1552, 1553, 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1568, 1569, 1570, 1571, 1572, 1573, 1574, 1575, 1576, 1577, 1578, 1579, 1580, 1581, 1582, 1583, 1584, 1585, 1586, 1587, 1588, 1589])]; + tensor scatter_34_mode_0 = const()[name = tensor("scatter_34_mode_0"), val = tensor("update")]; + tensor scatter_34_axis_0 = const()[name = tensor("scatter_34_axis_0"), val = tensor(0)]; + tensor scatter_34_validate_indices_0 = const()[name = tensor("scatter_34_validate_indices_0"), val = tensor(false)]; + tensor scatter_34_cast_fp16 = scatter(axis = scatter_34_axis_0, data = scatter_32_cast_fp16, indices = slice_by_index_34, mode = scatter_34_mode_0, updates = var_799_cast_fp16, validate_indices = scatter_34_validate_indices_0)[name = tensor("scatter_34_cast_fp16")]; + tensor var_809_begin_0 = const()[name = tensor("op_809_begin_0"), val = tensor([1001])]; + tensor var_809_end_0 = const()[name = tensor("op_809_end_0"), val = tensor([1590])]; + tensor var_809_end_mask_0 = const()[name = tensor("op_809_end_mask_0"), val = tensor([false])]; + tensor var_809_cast_fp16 = slice_by_index(begin = var_809_begin_0, end = var_809_end_0, end_mask = var_809_end_mask_0, x = scatter_33_cast_fp16)[name = tensor("op_809_cast_fp16")]; + tensor var_810_to_fp16 = const()[name = tensor("op_810_to_fp16"), val = tensor(0x1p+0)]; + tensor var_812_cast_fp16 = add(x = var_809_cast_fp16, y = var_810_to_fp16)[name = tensor("op_812_cast_fp16")]; + tensor scatter_35_mode_0 = const()[name = tensor("scatter_35_mode_0"), val = tensor("update")]; + tensor scatter_35_axis_0 = const()[name = tensor("scatter_35_axis_0"), val = tensor(0)]; + tensor scatter_35_validate_indices_0 = const()[name = tensor("scatter_35_validate_indices_0"), val = tensor(false)]; + tensor scatter_35_cast_fp16 = scatter(axis = scatter_35_axis_0, data = scatter_33_cast_fp16, indices = slice_by_index_34, mode = scatter_35_mode_0, updates = var_812_cast_fp16, validate_indices = scatter_35_validate_indices_0)[name = tensor("scatter_35_cast_fp16")]; + tensor var_829_begin_0 = const()[name = tensor("op_829_begin_0"), val = tensor([1060])]; + tensor var_829_end_0 = const()[name = tensor("op_829_end_0"), val = tensor([1649])]; + tensor var_829_end_mask_0 = const()[name = tensor("op_829_end_mask_0"), val = tensor([false])]; + tensor var_829_cast_fp16 = slice_by_index(begin = var_829_begin_0, end = var_829_end_0, end_mask = var_829_end_mask_0, x = scatter_34_cast_fp16)[name = tensor("op_829_cast_fp16")]; + tensor var_832_begin_0 = const()[name = tensor("op_832_begin_0"), val = tensor([18, 0])]; + tensor var_832_end_0 = const()[name = tensor("op_832_end_0"), val = tensor([19, 589])]; + tensor var_832_end_mask_0 = const()[name = tensor("op_832_end_mask_0"), val = tensor([false, true])]; + tensor var_832_squeeze_mask_0 = const()[name = tensor("op_832_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_832_cast_fp16 = slice_by_index(begin = var_832_begin_0, end = var_832_end_0, end_mask = var_832_end_mask_0, squeeze_mask = var_832_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_832_cast_fp16")]; + tensor var_834_cast_fp16 = add(x = var_829_cast_fp16, y = var_832_cast_fp16)[name = tensor("op_834_cast_fp16")]; + tensor slice_by_index_36 = const()[name = tensor("slice_by_index_36"), val = tensor([1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523, 1524, 1525, 1526, 1527, 1528, 1529, 1530, 1531, 1532, 1533, 1534, 1535, 1536, 1537, 1538, 1539, 1540, 1541, 1542, 1543, 1544, 1545, 1546, 1547, 1548, 1549, 1550, 1551, 1552, 1553, 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1568, 1569, 1570, 1571, 1572, 1573, 1574, 1575, 1576, 1577, 1578, 1579, 1580, 1581, 1582, 1583, 1584, 1585, 1586, 1587, 1588, 1589, 1590, 1591, 1592, 1593, 1594, 1595, 1596, 1597, 1598, 1599, 1600, 1601, 1602, 1603, 1604, 1605, 1606, 1607, 1608, 1609, 1610, 1611, 1612, 1613, 1614, 1615, 1616, 1617, 1618, 1619, 1620, 1621, 1622, 1623, 1624, 1625, 1626, 1627, 1628, 1629, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1638, 1639, 1640, 1641, 1642, 1643, 1644, 1645, 1646, 1647, 1648])]; + tensor scatter_36_mode_0 = const()[name = tensor("scatter_36_mode_0"), val = tensor("update")]; + tensor scatter_36_axis_0 = const()[name = tensor("scatter_36_axis_0"), val = tensor(0)]; + tensor scatter_36_validate_indices_0 = const()[name = tensor("scatter_36_validate_indices_0"), val = tensor(false)]; + tensor scatter_36_cast_fp16 = scatter(axis = scatter_36_axis_0, data = scatter_34_cast_fp16, indices = slice_by_index_36, mode = scatter_36_mode_0, updates = var_834_cast_fp16, validate_indices = scatter_36_validate_indices_0)[name = tensor("scatter_36_cast_fp16")]; + tensor var_844_begin_0 = const()[name = tensor("op_844_begin_0"), val = tensor([1060])]; + tensor var_844_end_0 = const()[name = tensor("op_844_end_0"), val = tensor([1649])]; + tensor var_844_end_mask_0 = const()[name = tensor("op_844_end_mask_0"), val = tensor([false])]; + tensor var_844_cast_fp16 = slice_by_index(begin = var_844_begin_0, end = var_844_end_0, end_mask = var_844_end_mask_0, x = scatter_35_cast_fp16)[name = tensor("op_844_cast_fp16")]; + tensor var_845_to_fp16 = const()[name = tensor("op_845_to_fp16"), val = tensor(0x1p+0)]; + tensor var_847_cast_fp16 = add(x = var_844_cast_fp16, y = var_845_to_fp16)[name = tensor("op_847_cast_fp16")]; + tensor scatter_37_mode_0 = const()[name = tensor("scatter_37_mode_0"), val = tensor("update")]; + tensor scatter_37_axis_0 = const()[name = tensor("scatter_37_axis_0"), val = tensor(0)]; + tensor scatter_37_validate_indices_0 = const()[name = tensor("scatter_37_validate_indices_0"), val = tensor(false)]; + tensor scatter_37_cast_fp16 = scatter(axis = scatter_37_axis_0, data = scatter_35_cast_fp16, indices = slice_by_index_36, mode = scatter_37_mode_0, updates = var_847_cast_fp16, validate_indices = scatter_37_validate_indices_0)[name = tensor("scatter_37_cast_fp16")]; + tensor var_864_begin_0 = const()[name = tensor("op_864_begin_0"), val = tensor([1119])]; + tensor var_864_end_0 = const()[name = tensor("op_864_end_0"), val = tensor([1708])]; + tensor var_864_end_mask_0 = const()[name = tensor("op_864_end_mask_0"), val = tensor([false])]; + tensor var_864_cast_fp16 = slice_by_index(begin = var_864_begin_0, end = var_864_end_0, end_mask = var_864_end_mask_0, x = scatter_36_cast_fp16)[name = tensor("op_864_cast_fp16")]; + tensor var_867_begin_0 = const()[name = tensor("op_867_begin_0"), val = tensor([19, 0])]; + tensor var_867_end_0 = const()[name = tensor("op_867_end_0"), val = tensor([20, 589])]; + tensor var_867_end_mask_0 = const()[name = tensor("op_867_end_mask_0"), val = tensor([false, true])]; + tensor var_867_squeeze_mask_0 = const()[name = tensor("op_867_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_867_cast_fp16 = slice_by_index(begin = var_867_begin_0, end = var_867_end_0, end_mask = var_867_end_mask_0, squeeze_mask = var_867_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_867_cast_fp16")]; + tensor var_869_cast_fp16 = add(x = var_864_cast_fp16, y = var_867_cast_fp16)[name = tensor("op_869_cast_fp16")]; + tensor slice_by_index_38 = const()[name = tensor("slice_by_index_38"), val = tensor([1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523, 1524, 1525, 1526, 1527, 1528, 1529, 1530, 1531, 1532, 1533, 1534, 1535, 1536, 1537, 1538, 1539, 1540, 1541, 1542, 1543, 1544, 1545, 1546, 1547, 1548, 1549, 1550, 1551, 1552, 1553, 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1568, 1569, 1570, 1571, 1572, 1573, 1574, 1575, 1576, 1577, 1578, 1579, 1580, 1581, 1582, 1583, 1584, 1585, 1586, 1587, 1588, 1589, 1590, 1591, 1592, 1593, 1594, 1595, 1596, 1597, 1598, 1599, 1600, 1601, 1602, 1603, 1604, 1605, 1606, 1607, 1608, 1609, 1610, 1611, 1612, 1613, 1614, 1615, 1616, 1617, 1618, 1619, 1620, 1621, 1622, 1623, 1624, 1625, 1626, 1627, 1628, 1629, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1638, 1639, 1640, 1641, 1642, 1643, 1644, 1645, 1646, 1647, 1648, 1649, 1650, 1651, 1652, 1653, 1654, 1655, 1656, 1657, 1658, 1659, 1660, 1661, 1662, 1663, 1664, 1665, 1666, 1667, 1668, 1669, 1670, 1671, 1672, 1673, 1674, 1675, 1676, 1677, 1678, 1679, 1680, 1681, 1682, 1683, 1684, 1685, 1686, 1687, 1688, 1689, 1690, 1691, 1692, 1693, 1694, 1695, 1696, 1697, 1698, 1699, 1700, 1701, 1702, 1703, 1704, 1705, 1706, 1707])]; + tensor scatter_38_mode_0 = const()[name = tensor("scatter_38_mode_0"), val = tensor("update")]; + tensor scatter_38_axis_0 = const()[name = tensor("scatter_38_axis_0"), val = tensor(0)]; + tensor scatter_38_validate_indices_0 = const()[name = tensor("scatter_38_validate_indices_0"), val = tensor(false)]; + tensor scatter_38_cast_fp16 = scatter(axis = scatter_38_axis_0, data = scatter_36_cast_fp16, indices = slice_by_index_38, mode = scatter_38_mode_0, updates = var_869_cast_fp16, validate_indices = scatter_38_validate_indices_0)[name = tensor("scatter_38_cast_fp16")]; + tensor var_879_begin_0 = const()[name = tensor("op_879_begin_0"), val = tensor([1119])]; + tensor var_879_end_0 = const()[name = tensor("op_879_end_0"), val = tensor([1708])]; + tensor var_879_end_mask_0 = const()[name = tensor("op_879_end_mask_0"), val = tensor([false])]; + tensor var_879_cast_fp16 = slice_by_index(begin = var_879_begin_0, end = var_879_end_0, end_mask = var_879_end_mask_0, x = scatter_37_cast_fp16)[name = tensor("op_879_cast_fp16")]; + tensor var_880_to_fp16 = const()[name = tensor("op_880_to_fp16"), val = tensor(0x1p+0)]; + tensor var_882_cast_fp16 = add(x = var_879_cast_fp16, y = var_880_to_fp16)[name = tensor("op_882_cast_fp16")]; + tensor scatter_39_mode_0 = const()[name = tensor("scatter_39_mode_0"), val = tensor("update")]; + tensor scatter_39_axis_0 = const()[name = tensor("scatter_39_axis_0"), val = tensor(0)]; + tensor scatter_39_validate_indices_0 = const()[name = tensor("scatter_39_validate_indices_0"), val = tensor(false)]; + tensor scatter_39_cast_fp16 = scatter(axis = scatter_39_axis_0, data = scatter_37_cast_fp16, indices = slice_by_index_38, mode = scatter_39_mode_0, updates = var_882_cast_fp16, validate_indices = scatter_39_validate_indices_0)[name = tensor("scatter_39_cast_fp16")]; + tensor var_899_begin_0 = const()[name = tensor("op_899_begin_0"), val = tensor([1178])]; + tensor var_899_end_0 = const()[name = tensor("op_899_end_0"), val = tensor([1767])]; + tensor var_899_end_mask_0 = const()[name = tensor("op_899_end_mask_0"), val = tensor([false])]; + tensor var_899_cast_fp16 = slice_by_index(begin = var_899_begin_0, end = var_899_end_0, end_mask = var_899_end_mask_0, x = scatter_38_cast_fp16)[name = tensor("op_899_cast_fp16")]; + tensor var_902_begin_0 = const()[name = tensor("op_902_begin_0"), val = tensor([20, 0])]; + tensor var_902_end_0 = const()[name = tensor("op_902_end_0"), val = tensor([21, 589])]; + tensor var_902_end_mask_0 = const()[name = tensor("op_902_end_mask_0"), val = tensor([false, true])]; + tensor var_902_squeeze_mask_0 = const()[name = tensor("op_902_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_902_cast_fp16 = slice_by_index(begin = var_902_begin_0, end = var_902_end_0, end_mask = var_902_end_mask_0, squeeze_mask = var_902_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_902_cast_fp16")]; + tensor var_904_cast_fp16 = add(x = var_899_cast_fp16, y = var_902_cast_fp16)[name = tensor("op_904_cast_fp16")]; + tensor slice_by_index_40 = const()[name = tensor("slice_by_index_40"), val = tensor([1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 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1571, 1572, 1573, 1574, 1575, 1576, 1577, 1578, 1579, 1580, 1581, 1582, 1583, 1584, 1585, 1586, 1587, 1588, 1589, 1590, 1591, 1592, 1593, 1594, 1595, 1596, 1597, 1598, 1599, 1600, 1601, 1602, 1603, 1604, 1605, 1606, 1607, 1608, 1609, 1610, 1611, 1612, 1613, 1614, 1615, 1616, 1617, 1618, 1619, 1620, 1621, 1622, 1623, 1624, 1625, 1626, 1627, 1628, 1629, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1638, 1639, 1640, 1641, 1642, 1643, 1644, 1645, 1646, 1647, 1648, 1649, 1650, 1651, 1652, 1653, 1654, 1655, 1656, 1657, 1658, 1659, 1660, 1661, 1662, 1663, 1664, 1665, 1666, 1667, 1668, 1669, 1670, 1671, 1672, 1673, 1674, 1675, 1676, 1677, 1678, 1679, 1680, 1681, 1682, 1683, 1684, 1685, 1686, 1687, 1688, 1689, 1690, 1691, 1692, 1693, 1694, 1695, 1696, 1697, 1698, 1699, 1700, 1701, 1702, 1703, 1704, 1705, 1706, 1707, 1708, 1709, 1710, 1711, 1712, 1713, 1714, 1715, 1716, 1717, 1718, 1719, 1720, 1721, 1722, 1723, 1724, 1725, 1726, 1727, 1728, 1729, 1730, 1731, 1732, 1733, 1734, 1735, 1736, 1737, 1738, 1739, 1740, 1741, 1742, 1743, 1744, 1745, 1746, 1747, 1748, 1749, 1750, 1751, 1752, 1753, 1754, 1755, 1756, 1757, 1758, 1759, 1760, 1761, 1762, 1763, 1764, 1765, 1766])]; + tensor scatter_40_mode_0 = const()[name = tensor("scatter_40_mode_0"), val = tensor("update")]; + tensor scatter_40_axis_0 = const()[name = tensor("scatter_40_axis_0"), val = tensor(0)]; + tensor scatter_40_validate_indices_0 = const()[name = tensor("scatter_40_validate_indices_0"), val = tensor(false)]; + tensor scatter_40_cast_fp16 = scatter(axis = scatter_40_axis_0, data = scatter_38_cast_fp16, indices = slice_by_index_40, mode = scatter_40_mode_0, updates = var_904_cast_fp16, validate_indices = scatter_40_validate_indices_0)[name = tensor("scatter_40_cast_fp16")]; + tensor var_914_begin_0 = const()[name = tensor("op_914_begin_0"), val = tensor([1178])]; + tensor var_914_end_0 = const()[name = tensor("op_914_end_0"), val = tensor([1767])]; + tensor var_914_end_mask_0 = const()[name = tensor("op_914_end_mask_0"), val = tensor([false])]; + tensor var_914_cast_fp16 = slice_by_index(begin = var_914_begin_0, end = var_914_end_0, end_mask = var_914_end_mask_0, x = scatter_39_cast_fp16)[name = tensor("op_914_cast_fp16")]; + tensor var_915_to_fp16 = const()[name = tensor("op_915_to_fp16"), val = tensor(0x1p+0)]; + tensor var_917_cast_fp16 = add(x = var_914_cast_fp16, y = var_915_to_fp16)[name = tensor("op_917_cast_fp16")]; + tensor scatter_41_mode_0 = const()[name = tensor("scatter_41_mode_0"), val = tensor("update")]; + tensor scatter_41_axis_0 = const()[name = tensor("scatter_41_axis_0"), val = tensor(0)]; + tensor scatter_41_validate_indices_0 = const()[name = tensor("scatter_41_validate_indices_0"), val = tensor(false)]; + tensor scatter_41_cast_fp16 = scatter(axis = scatter_41_axis_0, data = scatter_39_cast_fp16, indices = slice_by_index_40, mode = scatter_41_mode_0, updates = var_917_cast_fp16, validate_indices = scatter_41_validate_indices_0)[name = tensor("scatter_41_cast_fp16")]; + tensor voice_activity = real_div(x = scatter_40_cast_fp16, y = scatter_41_cast_fp16)[name = tensor("op_924_cast_fp16")]; + tensor var_929_axes_0 = const()[name = tensor("op_929_axes_0"), val = tensor([1])]; + tensor var_929_keep_dims_0 = const()[name = tensor("op_929_keep_dims_0"), val = tensor(false)]; + tensor speaker_activity = reduce_sum(axes = var_929_axes_0, keep_dims = var_929_keep_dims_0, x = speaker_ids)[name = tensor("op_929_cast_fp16")]; + tensor var_934_axes_0 = const()[name = tensor("op_934_axes_0"), val = tensor([2])]; + tensor var_934_keep_dims_0 = const()[name = tensor("op_934_keep_dims_0"), val = tensor(false)]; + tensor var_934_cast_fp16 = reduce_sum(axes = var_934_axes_0, keep_dims = var_934_keep_dims_0, x = speaker_ids)[name = tensor("op_934_cast_fp16")]; + tensor var_935_to_fp16 = const()[name = tensor("op_935_to_fp16"), val = tensor(0x1p+0)]; + tensor var_936_cast_fp16 = greater(x = var_934_cast_fp16, y = var_935_to_fp16)[name = tensor("op_936_cast_fp16")]; + tensor cast_8_dtype_0 = const()[name = tensor("cast_8_dtype_0"), val = tensor("fp16")]; + tensor overlapped_speaker_activity = cast(dtype = cast_8_dtype_0, x = var_936_cast_fp16)[name = tensor("cast_0")]; + } -> (speaker_probs, speaker_ids, speaker_activity, overlapped_speaker_activity, voice_activity, sliding_window_waveform); +} \ No newline at end of file diff --git a/speaker_segmenter/pyannote-v3/W8A16/SpeakerSegmenter.mlmodelc/weights/weight.bin b/speaker_segmenter/pyannote-v3/W8A16/SpeakerSegmenter.mlmodelc/weights/weight.bin new file mode 100644 index 0000000000000000000000000000000000000000..63f67063b3f036bade44222fd605beaed229f569 --- /dev/null +++ b/speaker_segmenter/pyannote-v3/W8A16/SpeakerSegmenter.mlmodelc/weights/weight.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:75ff1725ef4e58dacf9176466ec274a8a13a6132c296d6b571fb78ddad5455c4 +size 1520986 diff --git a/speaker_segmenter/pyannote-v3/W8A32/LICENSE_NOTICE.txt b/speaker_segmenter/pyannote-v3/W8A32/LICENSE_NOTICE.txt new file mode 100644 index 0000000000000000000000000000000000000000..be2da6c6e6d746ab53f1b21eac16d611aed1193a --- /dev/null +++ b/speaker_segmenter/pyannote-v3/W8A32/LICENSE_NOTICE.txt @@ -0,0 +1,7 @@ +Argmax proprietary and confidential. Under NDA. + +Copyright 2024 Argmax, Inc. All rights reserved. + +Unauthorized access, copying, use, distribution, and or commercialization of this file, via any medium or means is strictly prohibited. + +Please contact Argmax for licensing information at info@argmaxinc.com. diff --git a/speaker_segmenter/pyannote-v3/W8A32/README.txt b/speaker_segmenter/pyannote-v3/W8A32/README.txt new file mode 100644 index 0000000000000000000000000000000000000000..b64b4cbd04f27df7f9a363b2379b86c4522db1ef --- /dev/null +++ b/speaker_segmenter/pyannote-v3/W8A32/README.txt @@ -0,0 +1,6 @@ +# License + +Original model weights: https://huggingface.co/pyannote/segmentation-3.0/blob/main/LICENSE +Argmax-optimized model asset (Assets with `.mlmodelc` extension): https://huggingface.co/argmaxinc/speakerkit-pro/blob/main/LICENSE_NOTICE.txt + +Please contact info@argmaxinc.com for licensing SpeakerKit Pro assets \ No newline at end of file diff --git a/speaker_segmenter/pyannote-v3/W8A32/SpeakerSegmenter.mlmodelc/analytics/coremldata.bin b/speaker_segmenter/pyannote-v3/W8A32/SpeakerSegmenter.mlmodelc/analytics/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..e82a5de76c808c72973a7374ab5ad7f16098b078 --- /dev/null +++ b/speaker_segmenter/pyannote-v3/W8A32/SpeakerSegmenter.mlmodelc/analytics/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9e48e51c5ed655166a4547c02073722460f601af47e980a3b90ef4a2d692df4e +size 243 diff --git a/speaker_segmenter/pyannote-v3/W8A32/SpeakerSegmenter.mlmodelc/coremldata.bin b/speaker_segmenter/pyannote-v3/W8A32/SpeakerSegmenter.mlmodelc/coremldata.bin new file mode 100644 index 0000000000000000000000000000000000000000..f2b073c92de697054229e1dd635d2735b585744f --- /dev/null +++ b/speaker_segmenter/pyannote-v3/W8A32/SpeakerSegmenter.mlmodelc/coremldata.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4ef444ce9ae2cc01b181b4bac94432b399ea561f5c99484782dda939bc1e66df +size 497 diff --git a/speaker_segmenter/pyannote-v3/W8A32/SpeakerSegmenter.mlmodelc/metadata.json b/speaker_segmenter/pyannote-v3/W8A32/SpeakerSegmenter.mlmodelc/metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..9d60284957db58c388408c7389c3efb80a753b9b --- /dev/null +++ b/speaker_segmenter/pyannote-v3/W8A32/SpeakerSegmenter.mlmodelc/metadata.json @@ -0,0 +1,133 @@ +[ + { + "metadataOutputVersion" : "3.0", + "storagePrecision" : "Mixed (Float16, Float32, Palettized (8 bits))", + "outputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 21 × 589 × 3)", + "shortDescription" : "", + "shape" : "[21, 589, 3]", + "name" : "speaker_probs", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 21 × 589 × 3)", + "shortDescription" : "", + "shape" : "[21, 589, 3]", + "name" : "speaker_ids", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 21 × 3)", + "shortDescription" : "", + "shape" : "[21, 3]", + "name" : "speaker_activity", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 21 × 589)", + "shortDescription" : "", + "shape" : "[21, 589]", + "name" : "overlapped_speaker_activity", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 1767)", + "shortDescription" : "", + "shape" : "[1767]", + "name" : "voice_activity", + "type" : "MultiArray" + }, + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 21 × 1 × 160000)", + "shortDescription" : "", + "shape" : "[21, 1, 160000]", + "name" : "sliding_window_waveform", + "type" : "MultiArray" + } + ], + "modelParameters" : [ + + ], + "specificationVersion" : 7, + "mlProgramOperationTypeHistogram" : { + "Transpose" : 2, + "Ios16.maxPool" : 3, + "Ios16.exp" : 1, + "Ios16.softmax" : 1, + "SlidingWindows" : 1, + "Ios16.linear" : 5, + "Ios16.add" : 40, + "Ios16.realDiv" : 1, + "Ios16.reduceMax" : 1, + "Ios16.reduceSum" : 2, + "Ios16.reduceArgmax" : 1, + "Ios16.greater" : 1, + "Ios16.log" : 1, + "ExpandDims" : 1, + "Ios16.instanceNorm" : 4, + "Ios16.cast" : 4, + "Ios16.conv" : 3, + "Ios16.constexprLutToDense" : 22, + "OneHot" : 1, + "Ios16.scatter" : 42, + "SliceByIndex" : 61, + "Ios16.abs" : 1, + "Ios16.lstm" : 4, + "Ios16.leakyRelu" : 5 + }, + "computePrecision" : "Mixed (Float16, Float32, Int32)", + "isUpdatable" : "0", + "stateSchema" : [ + + ], + "availability" : { + "macOS" : "13.0", + "tvOS" : "16.0", + "visionOS" : "1.0", + "watchOS" : "9.0", + "iOS" : "16.0", + "macCatalyst" : "16.0" + }, + "modelType" : { + "name" : "MLModelType_mlProgram" + }, + "userDefinedMetadata" : { + "com.github.apple.coremltools.source_dialect" : "TorchScript", + "com.github.apple.coremltools.source" : "torch==2.5.1", + "com.github.apple.coremltools.version" : "8.1" + }, + "inputSchema" : [ + { + "hasShapeFlexibility" : "0", + "isOptional" : "0", + "dataType" : "Float16", + "formattedType" : "MultiArray (Float16 480000)", + "shortDescription" : "", + "shape" : "[480000]", + "name" : "waveform", + "type" : "MultiArray" + } + ], + "generatedClassName" : "SpeakerSegmenter_8_bit", + "method" : "predict" + } +] \ No newline at end of file diff --git a/speaker_segmenter/pyannote-v3/W8A32/SpeakerSegmenter.mlmodelc/model.mil b/speaker_segmenter/pyannote-v3/W8A32/SpeakerSegmenter.mlmodelc/model.mil new file mode 100644 index 0000000000000000000000000000000000000000..56080440023eb40eff43397165a1ecd90933669d --- /dev/null +++ b/speaker_segmenter/pyannote-v3/W8A32/SpeakerSegmenter.mlmodelc/model.mil @@ -0,0 +1,648 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3401.3.1"}, {"coremlc-version", "3401.4.1"}})] +{ + func main(tensor waveform) { + tensor var_3 = const()[name = tensor("op_3"), val = tensor(0)]; + tensor sliding_windows_0_size_0 = const()[name = tensor("sliding_windows_0_size_0"), val = tensor(160000)]; + tensor sliding_windows_0_stride_0 = const()[name = tensor("sliding_windows_0_stride_0"), val = tensor(16000)]; + tensor sliding_windows_0_cast_fp16 = sliding_windows(axis = var_3, size = sliding_windows_0_size_0, stride = sliding_windows_0_stride_0, x = waveform)[name = tensor("sliding_windows_0_cast_fp16")]; + tensor input_1_axes_0 = const()[name = tensor("input_1_axes_0"), val = tensor([1])]; + tensor sliding_window_waveform = expand_dims(axes = input_1_axes_0, x = sliding_windows_0_cast_fp16)[name = tensor("input_1_cast_fp16")]; + tensor var_9 = const()[name = tensor("op_9"), val = tensor(-1)]; + tensor model_sincnet_wav_norm1d_weight_to_fp16 = const()[name = tensor("model_sincnet_wav_norm1d_weight_to_fp16"), val = tensor([0x1.44p-7])]; + tensor model_sincnet_wav_norm1d_bias_to_fp16 = const()[name = tensor("model_sincnet_wav_norm1d_bias_to_fp16"), val = tensor([0x1.734p-5])]; + tensor var_17_to_fp16 = const()[name = tensor("op_17_to_fp16"), val = tensor(0x1.5p-17)]; + tensor input_3_cast_fp16 = instance_norm(beta = model_sincnet_wav_norm1d_bias_to_fp16, epsilon = var_17_to_fp16, gamma = model_sincnet_wav_norm1d_weight_to_fp16, x = sliding_window_waveform)[name = tensor("input_3_cast_fp16")]; + tensor outputs_pad_type_0 = const()[name = tensor("outputs_pad_type_0"), val = tensor("valid")]; + tensor outputs_strides_0 = const()[name = tensor("outputs_strides_0"), val = tensor([10])]; + tensor outputs_pad_0 = const()[name = tensor("outputs_pad_0"), val = tensor([0, 0])]; + tensor outputs_dilations_0 = const()[name = tensor("outputs_dilations_0"), val = tensor([1])]; + tensor outputs_groups_0 = const()[name = tensor("outputs_groups_0"), val = tensor(1)]; + tensor model_sincnet_conv1d_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20224))), name = tensor("model_sincnet_conv1d_0_weight_to_fp16_palettized"), shape = tensor([80, 1, 251])]; + tensor outputs_cast_fp16 = conv(dilations = outputs_dilations_0, groups = outputs_groups_0, pad = outputs_pad_0, pad_type = outputs_pad_type_0, strides = outputs_strides_0, weight = model_sincnet_conv1d_0_weight_to_fp16_palettized, x = input_3_cast_fp16)[name = tensor("outputs_cast_fp16")]; + tensor input_5_cast_fp16 = abs(x = outputs_cast_fp16)[name = tensor("input_5_cast_fp16")]; + tensor var_57 = const()[name = tensor("op_57"), val = tensor([3])]; + tensor var_58 = const()[name = tensor("op_58"), val = tensor([3])]; + tensor input_7_pad_type_0 = const()[name = tensor("input_7_pad_type_0"), val = tensor("custom")]; + tensor input_7_pad_0 = const()[name = tensor("input_7_pad_0"), val = tensor([0, 0])]; + tensor input_7_ceil_mode_0 = const()[name = tensor("input_7_ceil_mode_0"), val = tensor(false)]; + tensor input_7_cast_fp16 = max_pool(ceil_mode = input_7_ceil_mode_0, kernel_sizes = var_57, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = var_58, x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; + tensor model_sincnet_norm1d_0_weight_to_fp16 = const()[name = tensor("model_sincnet_norm1d_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20800)))]; + tensor model_sincnet_norm1d_0_bias_to_fp16 = const()[name = tensor("model_sincnet_norm1d_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21056)))]; + tensor input_9_cast_fp16 = instance_norm(beta = model_sincnet_norm1d_0_bias_to_fp16, epsilon = var_17_to_fp16, gamma = model_sincnet_norm1d_0_weight_to_fp16, x = input_7_cast_fp16)[name = tensor("input_9_cast_fp16")]; + tensor var_26_to_fp16 = const()[name = tensor("op_26_to_fp16"), val = tensor(0x1.47cp-7)]; + tensor input_11_cast_fp16 = leaky_relu(alpha = var_26_to_fp16, x = input_9_cast_fp16)[name = tensor("input_11_cast_fp16")]; + tensor input_13_pad_type_0 = const()[name = tensor("input_13_pad_type_0"), val = tensor("valid")]; + tensor input_13_strides_0 = const()[name = tensor("input_13_strides_0"), val = tensor([1])]; + tensor input_13_pad_0 = const()[name = tensor("input_13_pad_0"), val = tensor([0, 0])]; + tensor input_13_dilations_0 = const()[name = tensor("input_13_dilations_0"), val = tensor([1])]; + tensor input_13_groups_0 = const()[name = tensor("input_13_groups_0"), val = tensor(1)]; + tensor model_sincnet_conv1d_1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21312))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45376))), name = tensor("model_sincnet_conv1d_1_weight_to_fp16_palettized"), shape = tensor([60, 80, 5])]; + tensor model_sincnet_conv1d_1_bias_to_fp16 = const()[name = tensor("model_sincnet_conv1d_1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45952)))]; + tensor input_13_cast_fp16 = conv(bias = model_sincnet_conv1d_1_bias_to_fp16, dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = model_sincnet_conv1d_1_weight_to_fp16_palettized, x = input_11_cast_fp16)[name = tensor("input_13_cast_fp16")]; + tensor var_73 = const()[name = tensor("op_73"), val = tensor([3])]; + tensor var_74 = const()[name = tensor("op_74"), val = tensor([3])]; + tensor input_15_pad_type_0 = const()[name = tensor("input_15_pad_type_0"), val = tensor("custom")]; + tensor input_15_pad_0 = const()[name = tensor("input_15_pad_0"), val = tensor([0, 0])]; + tensor input_15_ceil_mode_0 = const()[name = tensor("input_15_ceil_mode_0"), val = tensor(false)]; + tensor input_15_cast_fp16 = max_pool(ceil_mode = input_15_ceil_mode_0, kernel_sizes = var_73, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = var_74, x = input_13_cast_fp16)[name = tensor("input_15_cast_fp16")]; + tensor model_sincnet_norm1d_1_weight_to_fp16 = const()[name = tensor("model_sincnet_norm1d_1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46144)))]; + tensor model_sincnet_norm1d_1_bias_to_fp16 = const()[name = tensor("model_sincnet_norm1d_1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46336)))]; + tensor input_17_cast_fp16 = instance_norm(beta = model_sincnet_norm1d_1_bias_to_fp16, epsilon = var_17_to_fp16, gamma = model_sincnet_norm1d_1_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("input_17_cast_fp16")]; + tensor input_19_cast_fp16 = leaky_relu(alpha = var_26_to_fp16, x = input_17_cast_fp16)[name = tensor("input_19_cast_fp16")]; + tensor input_21_pad_type_0 = const()[name = tensor("input_21_pad_type_0"), val = tensor("valid")]; + tensor input_21_strides_0 = const()[name = tensor("input_21_strides_0"), val = tensor([1])]; + tensor input_21_pad_0 = const()[name = tensor("input_21_pad_0"), val = tensor([0, 0])]; + tensor input_21_dilations_0 = const()[name = tensor("input_21_dilations_0"), val = tensor([1])]; + tensor input_21_groups_0 = const()[name = tensor("input_21_groups_0"), val = tensor(1)]; + tensor model_sincnet_conv1d_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64640))), name = tensor("model_sincnet_conv1d_2_weight_to_fp16_palettized"), shape = tensor([60, 60, 5])]; + tensor model_sincnet_conv1d_2_bias_to_fp16 = const()[name = tensor("model_sincnet_conv1d_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65216)))]; + tensor input_21_cast_fp16 = conv(bias = model_sincnet_conv1d_2_bias_to_fp16, dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = model_sincnet_conv1d_2_weight_to_fp16_palettized, x = input_19_cast_fp16)[name = tensor("input_21_cast_fp16")]; + tensor var_89 = const()[name = tensor("op_89"), val = tensor([3])]; + tensor var_90 = const()[name = tensor("op_90"), val = tensor([3])]; + tensor input_23_pad_type_0 = const()[name = tensor("input_23_pad_type_0"), val = tensor("custom")]; + tensor input_23_pad_0 = const()[name = tensor("input_23_pad_0"), val = tensor([0, 0])]; + tensor input_23_ceil_mode_0 = const()[name = tensor("input_23_ceil_mode_0"), val = tensor(false)]; + tensor input_23_cast_fp16 = max_pool(ceil_mode = input_23_ceil_mode_0, kernel_sizes = var_89, pad = input_23_pad_0, pad_type = input_23_pad_type_0, strides = var_90, x = input_21_cast_fp16)[name = tensor("input_23_cast_fp16")]; + tensor model_sincnet_norm1d_2_weight_to_fp16 = const()[name = tensor("model_sincnet_norm1d_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65408)))]; + tensor model_sincnet_norm1d_2_bias_to_fp16 = const()[name = tensor("model_sincnet_norm1d_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65600)))]; + tensor input_25_cast_fp16 = instance_norm(beta = model_sincnet_norm1d_2_bias_to_fp16, epsilon = var_17_to_fp16, gamma = model_sincnet_norm1d_2_weight_to_fp16, x = input_23_cast_fp16)[name = tensor("input_25_cast_fp16")]; + tensor x_cast_fp16 = leaky_relu(alpha = var_26_to_fp16, x = input_25_cast_fp16)[name = tensor("x_cast_fp16")]; + tensor transpose_2_perm_0 = const()[name = tensor("transpose_2_perm_0"), val = tensor([2, 0, 1])]; + tensor transpose_2_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("transpose_2_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor add_0 = const()[name = tensor("add_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65792)))]; + tensor add_1 = const()[name = tensor("add_1"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(67904)))]; + tensor concat_4_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100800))), name = tensor("concat_4_palettized"), shape = tensor([512, 60])]; + tensor concat_5_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(167488))), name = tensor("concat_5_palettized"), shape = tensor([512, 128])]; + tensor concat_6_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(168576))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199360))), name = tensor("concat_6_palettized"), shape = tensor([512, 60])]; + tensor concat_7_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(200448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(266048))), name = tensor("concat_7_palettized"), shape = tensor([512, 128])]; + tensor input_29_lstm_layer_0_lstm_h0_reshaped_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(272576))), name = tensor("input_29_lstm_layer_0_lstm_h0_reshaped_palettized"), shape = tensor([21, 256])]; + tensor input_29_lstm_layer_0_direction_0 = const()[name = tensor("input_29_lstm_layer_0_direction_0"), val = tensor("bidirectional")]; + tensor input_29_lstm_layer_0_output_sequence_0 = const()[name = tensor("input_29_lstm_layer_0_output_sequence_0"), val = tensor(true)]; + tensor input_29_lstm_layer_0_recurrent_activation_0 = const()[name = tensor("input_29_lstm_layer_0_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_29_lstm_layer_0_cell_activation_0 = const()[name = tensor("input_29_lstm_layer_0_cell_activation_0"), val = tensor("tanh")]; + tensor input_29_lstm_layer_0_activation_0 = const()[name = tensor("input_29_lstm_layer_0_activation_0"), val = tensor("tanh")]; + tensor transpose_2_cast_fp16 = transpose(perm = transpose_2_perm_0, x = x_cast_fp16)[name = tensor("transpose_1")]; + tensor transpose_2_cast_fp16_to_fp32 = cast(dtype = transpose_2_cast_fp16_to_fp32_dtype_0, x = transpose_2_cast_fp16)[name = tensor("cast_3")]; + tensor input_29_lstm_layer_0_0, tensor input_29_lstm_layer_0_1, tensor input_29_lstm_layer_0_2 = lstm(activation = input_29_lstm_layer_0_activation_0, bias = add_0, bias_back = add_1, cell_activation = input_29_lstm_layer_0_cell_activation_0, direction = input_29_lstm_layer_0_direction_0, initial_c = input_29_lstm_layer_0_lstm_h0_reshaped_palettized, initial_h = input_29_lstm_layer_0_lstm_h0_reshaped_palettized, output_sequence = input_29_lstm_layer_0_output_sequence_0, recurrent_activation = input_29_lstm_layer_0_recurrent_activation_0, weight_hh = concat_5_palettized, weight_hh_back = concat_7_palettized, weight_ih = concat_4_palettized, weight_ih_back = concat_6_palettized, x = transpose_2_cast_fp16_to_fp32)[name = tensor("input_29_lstm_layer_0")]; + tensor add_2 = const()[name = tensor("add_2"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(273664)))]; + tensor add_3 = const()[name = tensor("add_3"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(275776)))]; + tensor concat_14_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(277888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(409024))), name = tensor("concat_14_palettized"), shape = tensor([512, 256])]; + tensor concat_15_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(410112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(475712))), name = tensor("concat_15_palettized"), shape = tensor([512, 128])]; + tensor concat_16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(476800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607936))), name = tensor("concat_16_palettized"), shape = tensor([512, 256])]; + tensor concat_17_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(609024))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(674624))), name = tensor("concat_17_palettized"), shape = tensor([512, 128])]; + tensor input_29_lstm_layer_1_direction_0 = const()[name = tensor("input_29_lstm_layer_1_direction_0"), val = tensor("bidirectional")]; + tensor input_29_lstm_layer_1_output_sequence_0 = const()[name = tensor("input_29_lstm_layer_1_output_sequence_0"), val = tensor(true)]; + tensor input_29_lstm_layer_1_recurrent_activation_0 = const()[name = tensor("input_29_lstm_layer_1_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_29_lstm_layer_1_cell_activation_0 = const()[name = tensor("input_29_lstm_layer_1_cell_activation_0"), val = tensor("tanh")]; + tensor input_29_lstm_layer_1_activation_0 = const()[name = tensor("input_29_lstm_layer_1_activation_0"), val = tensor("tanh")]; + tensor input_29_lstm_layer_1_0, tensor input_29_lstm_layer_1_1, tensor input_29_lstm_layer_1_2 = lstm(activation = input_29_lstm_layer_1_activation_0, bias = add_2, bias_back = add_3, cell_activation = input_29_lstm_layer_1_cell_activation_0, direction = input_29_lstm_layer_1_direction_0, initial_c = input_29_lstm_layer_0_lstm_h0_reshaped_palettized, initial_h = input_29_lstm_layer_0_lstm_h0_reshaped_palettized, output_sequence = input_29_lstm_layer_1_output_sequence_0, recurrent_activation = input_29_lstm_layer_1_recurrent_activation_0, weight_hh = concat_15_palettized, weight_hh_back = concat_17_palettized, weight_ih = concat_14_palettized, weight_ih_back = concat_16_palettized, x = input_29_lstm_layer_0_0)[name = tensor("input_29_lstm_layer_1")]; + tensor add_4 = const()[name = tensor("add_4"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(675712)))]; + tensor add_5 = const()[name = tensor("add_5"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(677824)))]; + tensor concat_24_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(679936))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(811072))), name = tensor("concat_24_palettized"), shape = tensor([512, 256])]; + tensor concat_25_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(812160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(877760))), name = tensor("concat_25_palettized"), shape = tensor([512, 128])]; + tensor concat_26_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(878848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1009984))), name = tensor("concat_26_palettized"), shape = tensor([512, 256])]; + tensor concat_27_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1011072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1076672))), name = tensor("concat_27_palettized"), shape = tensor([512, 128])]; + tensor input_29_lstm_layer_2_direction_0 = const()[name = tensor("input_29_lstm_layer_2_direction_0"), val = tensor("bidirectional")]; + tensor input_29_lstm_layer_2_output_sequence_0 = const()[name = tensor("input_29_lstm_layer_2_output_sequence_0"), val = tensor(true)]; + tensor input_29_lstm_layer_2_recurrent_activation_0 = const()[name = tensor("input_29_lstm_layer_2_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_29_lstm_layer_2_cell_activation_0 = const()[name = tensor("input_29_lstm_layer_2_cell_activation_0"), val = tensor("tanh")]; + tensor input_29_lstm_layer_2_activation_0 = const()[name = tensor("input_29_lstm_layer_2_activation_0"), val = tensor("tanh")]; + tensor input_29_lstm_layer_2_0, tensor input_29_lstm_layer_2_1, tensor input_29_lstm_layer_2_2 = lstm(activation = input_29_lstm_layer_2_activation_0, bias = add_4, bias_back = add_5, cell_activation = input_29_lstm_layer_2_cell_activation_0, direction = input_29_lstm_layer_2_direction_0, initial_c = input_29_lstm_layer_0_lstm_h0_reshaped_palettized, initial_h = input_29_lstm_layer_0_lstm_h0_reshaped_palettized, output_sequence = input_29_lstm_layer_2_output_sequence_0, recurrent_activation = input_29_lstm_layer_2_recurrent_activation_0, weight_hh = concat_25_palettized, weight_hh_back = concat_27_palettized, weight_ih = concat_24_palettized, weight_ih_back = concat_26_palettized, x = input_29_lstm_layer_1_0)[name = tensor("input_29_lstm_layer_2")]; + tensor add_6 = const()[name = tensor("add_6"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1077760)))]; + tensor add_7 = const()[name = tensor("add_7"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1079872)))]; + tensor concat_34_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1081984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1213120))), name = tensor("concat_34_palettized"), shape = tensor([512, 256])]; + tensor concat_35_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1214208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1279808))), name = tensor("concat_35_palettized"), shape = tensor([512, 128])]; + tensor concat_36_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1280896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1412032))), name = tensor("concat_36_palettized"), shape = tensor([512, 256])]; + tensor concat_37_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1413120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1478720))), name = tensor("concat_37_palettized"), shape = tensor([512, 128])]; + tensor input_29_batch_first_direction_0 = const()[name = tensor("input_29_batch_first_direction_0"), val = tensor("bidirectional")]; + tensor input_29_batch_first_output_sequence_0 = const()[name = tensor("input_29_batch_first_output_sequence_0"), val = tensor(true)]; + tensor input_29_batch_first_recurrent_activation_0 = const()[name = tensor("input_29_batch_first_recurrent_activation_0"), val = tensor("sigmoid")]; + tensor input_29_batch_first_cell_activation_0 = const()[name = tensor("input_29_batch_first_cell_activation_0"), val = tensor("tanh")]; + tensor input_29_batch_first_activation_0 = const()[name = tensor("input_29_batch_first_activation_0"), val = tensor("tanh")]; + tensor input_29_batch_first_0, tensor input_29_batch_first_1, tensor input_29_batch_first_2 = lstm(activation = input_29_batch_first_activation_0, bias = add_6, bias_back = add_7, cell_activation = input_29_batch_first_cell_activation_0, direction = input_29_batch_first_direction_0, initial_c = input_29_lstm_layer_0_lstm_h0_reshaped_palettized, initial_h = input_29_lstm_layer_0_lstm_h0_reshaped_palettized, output_sequence = input_29_batch_first_output_sequence_0, recurrent_activation = input_29_batch_first_recurrent_activation_0, weight_hh = concat_35_palettized, weight_hh_back = concat_37_palettized, weight_ih = concat_34_palettized, weight_ih_back = concat_36_palettized, x = input_29_lstm_layer_2_0)[name = tensor("input_29_batch_first")]; + tensor input_29_perm_0 = const()[name = tensor("input_29_perm_0"), val = tensor([1, 0, 2])]; + tensor input_29_batch_first_0_to_fp16_dtype_0 = const()[name = tensor("input_29_batch_first_0_to_fp16_dtype_0"), val = tensor("fp16")]; + tensor model_linear_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1479808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1512640))), name = tensor("model_linear_0_weight_to_fp16_palettized"), shape = tensor([128, 256])]; + tensor model_linear_0_bias_to_fp16 = const()[name = tensor("model_linear_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1513216)))]; + tensor input_29_batch_first_0_to_fp16 = cast(dtype = input_29_batch_first_0_to_fp16_dtype_0, x = input_29_batch_first_0)[name = tensor("cast_2")]; + tensor input_29_cast_fp16 = transpose(perm = input_29_perm_0, x = input_29_batch_first_0_to_fp16)[name = tensor("transpose_0")]; + tensor linear_0_cast_fp16 = linear(bias = model_linear_0_bias_to_fp16, weight = model_linear_0_weight_to_fp16_palettized, x = input_29_cast_fp16)[name = tensor("linear_0_cast_fp16")]; + tensor input_33_cast_fp16 = leaky_relu(alpha = var_26_to_fp16, x = linear_0_cast_fp16)[name = tensor("input_33_cast_fp16")]; + tensor model_linear_1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1513536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1529984))), name = tensor("model_linear_1_weight_to_fp16_palettized"), shape = tensor([128, 128])]; + tensor model_linear_1_bias_to_fp16 = const()[name = tensor("model_linear_1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1530560)))]; + tensor linear_1_cast_fp16 = linear(bias = model_linear_1_bias_to_fp16, weight = model_linear_1_weight_to_fp16_palettized, x = input_33_cast_fp16)[name = tensor("linear_1_cast_fp16")]; + tensor input_37_cast_fp16 = leaky_relu(alpha = var_26_to_fp16, x = linear_1_cast_fp16)[name = tensor("input_37_cast_fp16")]; + tensor model_classifier_weight_to_fp16 = const()[name = tensor("model_classifier_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1530880)))]; + tensor model_classifier_bias_to_fp16 = const()[name = tensor("model_classifier_bias_to_fp16"), val = tensor([-0x1.01p+0, 0x1.67cp-2, 0x1.3d8p-1, 0x1.c8cp-2, -0x1.444p-2, -0x1.59p-1, -0x1.8fcp-2])]; + tensor linear_2_cast_fp16 = linear(bias = model_classifier_bias_to_fp16, weight = model_classifier_weight_to_fp16, x = input_37_cast_fp16)[name = tensor("linear_2_cast_fp16")]; + tensor powerset_softmax_cast_fp16 = softmax(axis = var_9, x = linear_2_cast_fp16)[name = tensor("powerset_softmax_cast_fp16")]; + tensor powerset_epsilon_0_to_fp16 = const()[name = tensor("powerset_epsilon_0_to_fp16"), val = tensor(0x0p+0)]; + tensor powerset_cast_fp16 = log(epsilon = powerset_epsilon_0_to_fp16, x = powerset_softmax_cast_fp16)[name = tensor("powerset_cast_fp16")]; + tensor powerset_probs_1_cast_fp16 = exp(x = powerset_cast_fp16)[name = tensor("powerset_probs_1_cast_fp16")]; + tensor transpose_0_to_fp16 = const()[name = tensor("transpose_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1532736)))]; + tensor speaker_probs_bias_0_to_fp16 = const()[name = tensor("speaker_probs_bias_0_to_fp16"), val = tensor([0x0p+0, 0x0p+0, 0x0p+0])]; + tensor speaker_probs = linear(bias = speaker_probs_bias_0_to_fp16, weight = transpose_0_to_fp16, x = powerset_probs_1_cast_fp16)[name = tensor("speaker_probs_cast_fp16")]; + tensor var_157 = const()[name = tensor("op_157"), val = tensor(-1)]; + tensor var_158 = const()[name = tensor("op_158"), val = tensor(false)]; + tensor var_159_cast_fp16 = reduce_argmax(axis = var_157, keep_dims = var_158, x = powerset_cast_fp16)[name = tensor("op_159_cast_fp16")]; + tensor var_161_one_hot_vector_size_0 = const()[name = tensor("op_161_one_hot_vector_size_0"), val = tensor(7)]; + tensor var_161_axis_0 = const()[name = tensor("op_161_axis_0"), val = tensor(-1)]; + tensor var_161_on_value_0 = const()[name = tensor("op_161_on_value_0"), val = tensor(1)]; + tensor var_161_off_value_0 = const()[name = tensor("op_161_off_value_0"), val = tensor(0)]; + tensor var_161 = one_hot(axis = var_161_axis_0, indices = var_159_cast_fp16, off_value = var_161_off_value_0, on_value = var_161_on_value_0, one_hot_vector_size = var_161_one_hot_vector_size_0)[name = tensor("op_161")]; + tensor cast_1_to_fp16_dtype_0 = const()[name = tensor("cast_1_to_fp16_dtype_0"), val = tensor("fp16")]; + tensor transpose_1_to_fp16 = const()[name = tensor("transpose_1_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1532864)))]; + tensor speaker_ids_bias_0_to_fp16 = const()[name = tensor("speaker_ids_bias_0_to_fp16"), val = tensor([0x0p+0, 0x0p+0, 0x0p+0])]; + tensor var_161_to_fp16 = cast(dtype = cast_1_to_fp16_dtype_0, x = var_161)[name = tensor("cast_1")]; + tensor speaker_ids = linear(bias = speaker_ids_bias_0_to_fp16, weight = transpose_1_to_fp16, x = var_161_to_fp16)[name = tensor("speaker_ids_cast_fp16")]; + tensor reduce_max_0_axes_0 = const()[name = tensor("reduce_max_0_axes_0"), val = tensor([-1])]; + tensor reduce_max_0_keep_dims_0 = const()[name = tensor("reduce_max_0_keep_dims_0"), val = tensor(false)]; + tensor reduce_max_0_cast_fp16 = reduce_max(axes = reduce_max_0_axes_0, keep_dims = reduce_max_0_keep_dims_0, x = speaker_probs)[name = tensor("reduce_max_0_cast_fp16")]; + tensor var_202_begin_0 = const()[name = tensor("op_202_begin_0"), val = tensor([0, 0])]; + tensor var_202_end_0 = const()[name = tensor("op_202_end_0"), val = tensor([1, 589])]; + tensor var_202_end_mask_0 = const()[name = tensor("op_202_end_mask_0"), val = tensor([false, true])]; + tensor var_202_squeeze_mask_0 = const()[name = tensor("op_202_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_202_cast_fp16 = slice_by_index(begin = var_202_begin_0, end = var_202_end_0, end_mask = var_202_end_mask_0, squeeze_mask = var_202_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_202_cast_fp16")]; + tensor slice_by_index_0 = const()[name = tensor("slice_by_index_0"), val = tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588])]; + tensor scatter_0_mode_0 = const()[name = tensor("scatter_0_mode_0"), val = tensor("update")]; + tensor scatter_0_axis_0 = const()[name = tensor("scatter_0_axis_0"), val = tensor(0)]; + tensor _aggregated_voice_activity_to_fp16 = const()[name = tensor("_aggregated_voice_activity_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1532992)))]; + tensor scatter_0_cast_fp16 = scatter(axis = scatter_0_axis_0, data = _aggregated_voice_activity_to_fp16, indices = slice_by_index_0, mode = scatter_0_mode_0, updates = var_202_cast_fp16)[name = tensor("scatter_0_cast_fp16")]; + tensor scatter_1_mode_0 = const()[name = tensor("scatter_1_mode_0"), val = tensor("update")]; + tensor scatter_1_axis_0 = const()[name = tensor("scatter_1_axis_0"), val = tensor(0)]; + tensor var_217_to_fp16 = const()[name = tensor("op_217_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1536640)))]; + tensor scatter_1_cast_fp16 = scatter(axis = scatter_1_axis_0, data = _aggregated_voice_activity_to_fp16, indices = slice_by_index_0, mode = scatter_1_mode_0, updates = var_217_to_fp16)[name = tensor("scatter_1_cast_fp16")]; + tensor var_234_begin_0 = const()[name = tensor("op_234_begin_0"), val = tensor([58])]; + tensor var_234_end_0 = const()[name = tensor("op_234_end_0"), val = tensor([647])]; + tensor var_234_end_mask_0 = const()[name = tensor("op_234_end_mask_0"), val = tensor([false])]; + tensor var_234_cast_fp16 = slice_by_index(begin = var_234_begin_0, end = var_234_end_0, end_mask = var_234_end_mask_0, x = scatter_0_cast_fp16)[name = tensor("op_234_cast_fp16")]; + tensor var_237_begin_0 = const()[name = tensor("op_237_begin_0"), val = tensor([1, 0])]; + tensor var_237_end_0 = const()[name = tensor("op_237_end_0"), val = tensor([2, 589])]; + tensor var_237_end_mask_0 = const()[name = tensor("op_237_end_mask_0"), val = tensor([false, true])]; + tensor var_237_squeeze_mask_0 = const()[name = tensor("op_237_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_237_cast_fp16 = slice_by_index(begin = var_237_begin_0, end = var_237_end_0, end_mask = var_237_end_mask_0, squeeze_mask = var_237_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_237_cast_fp16")]; + tensor var_239_cast_fp16 = add(x = var_234_cast_fp16, y = var_237_cast_fp16)[name = tensor("op_239_cast_fp16")]; + tensor slice_by_index_2 = const()[name = tensor("slice_by_index_2"), val = tensor([58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646])]; + tensor scatter_2_mode_0 = const()[name = tensor("scatter_2_mode_0"), val = tensor("update")]; + tensor scatter_2_axis_0 = const()[name = tensor("scatter_2_axis_0"), val = tensor(0)]; + tensor scatter_2_cast_fp16 = scatter(axis = scatter_2_axis_0, data = scatter_0_cast_fp16, indices = slice_by_index_2, mode = scatter_2_mode_0, updates = var_239_cast_fp16)[name = tensor("scatter_2_cast_fp16")]; + tensor var_249_begin_0 = const()[name = tensor("op_249_begin_0"), val = tensor([58])]; + tensor var_249_end_0 = const()[name = tensor("op_249_end_0"), val = tensor([647])]; + tensor var_249_end_mask_0 = const()[name = tensor("op_249_end_mask_0"), val = tensor([false])]; + tensor var_249_cast_fp16 = slice_by_index(begin = var_249_begin_0, end = var_249_end_0, end_mask = var_249_end_mask_0, x = scatter_1_cast_fp16)[name = tensor("op_249_cast_fp16")]; + tensor var_250_to_fp16 = const()[name = tensor("op_250_to_fp16"), val = tensor(0x1p+0)]; + tensor var_252_cast_fp16 = add(x = var_249_cast_fp16, y = var_250_to_fp16)[name = tensor("op_252_cast_fp16")]; + tensor scatter_3_mode_0 = const()[name = tensor("scatter_3_mode_0"), val = tensor("update")]; + tensor scatter_3_axis_0 = const()[name = tensor("scatter_3_axis_0"), val = tensor(0)]; + tensor scatter_3_cast_fp16 = scatter(axis = scatter_3_axis_0, data = scatter_1_cast_fp16, indices = slice_by_index_2, mode = scatter_3_mode_0, updates = var_252_cast_fp16)[name = tensor("scatter_3_cast_fp16")]; + tensor var_269_begin_0 = const()[name = tensor("op_269_begin_0"), val = tensor([117])]; + tensor var_269_end_0 = const()[name = tensor("op_269_end_0"), val = tensor([706])]; + tensor var_269_end_mask_0 = const()[name = tensor("op_269_end_mask_0"), val = tensor([false])]; + tensor var_269_cast_fp16 = slice_by_index(begin = var_269_begin_0, end = var_269_end_0, end_mask = var_269_end_mask_0, x = scatter_2_cast_fp16)[name = tensor("op_269_cast_fp16")]; + tensor var_272_begin_0 = const()[name = tensor("op_272_begin_0"), val = tensor([2, 0])]; + tensor var_272_end_0 = const()[name = tensor("op_272_end_0"), val = tensor([3, 589])]; + tensor var_272_end_mask_0 = const()[name = tensor("op_272_end_mask_0"), val = tensor([false, true])]; + tensor var_272_squeeze_mask_0 = const()[name = tensor("op_272_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_272_cast_fp16 = slice_by_index(begin = var_272_begin_0, end = var_272_end_0, end_mask = var_272_end_mask_0, squeeze_mask = var_272_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_272_cast_fp16")]; + tensor var_274_cast_fp16 = add(x = var_269_cast_fp16, y = var_272_cast_fp16)[name = tensor("op_274_cast_fp16")]; + tensor slice_by_index_4 = const()[name = tensor("slice_by_index_4"), val = tensor([117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705])]; + tensor scatter_4_mode_0 = const()[name = tensor("scatter_4_mode_0"), val = tensor("update")]; + tensor scatter_4_axis_0 = const()[name = tensor("scatter_4_axis_0"), val = tensor(0)]; + tensor scatter_4_cast_fp16 = scatter(axis = scatter_4_axis_0, data = scatter_2_cast_fp16, indices = slice_by_index_4, mode = scatter_4_mode_0, updates = var_274_cast_fp16)[name = tensor("scatter_4_cast_fp16")]; + tensor var_284_begin_0 = const()[name = tensor("op_284_begin_0"), val = tensor([117])]; + tensor var_284_end_0 = const()[name = tensor("op_284_end_0"), val = tensor([706])]; + tensor var_284_end_mask_0 = const()[name = tensor("op_284_end_mask_0"), val = tensor([false])]; + tensor var_284_cast_fp16 = slice_by_index(begin = var_284_begin_0, end = var_284_end_0, end_mask = var_284_end_mask_0, x = scatter_3_cast_fp16)[name = tensor("op_284_cast_fp16")]; + tensor var_285_to_fp16 = const()[name = tensor("op_285_to_fp16"), val = tensor(0x1p+0)]; + tensor var_287_cast_fp16 = add(x = var_284_cast_fp16, y = var_285_to_fp16)[name = tensor("op_287_cast_fp16")]; + tensor scatter_5_mode_0 = const()[name = tensor("scatter_5_mode_0"), val = tensor("update")]; + tensor scatter_5_axis_0 = const()[name = tensor("scatter_5_axis_0"), val = tensor(0)]; + tensor scatter_5_cast_fp16 = scatter(axis = scatter_5_axis_0, data = scatter_3_cast_fp16, indices = slice_by_index_4, mode = scatter_5_mode_0, updates = var_287_cast_fp16)[name = tensor("scatter_5_cast_fp16")]; + tensor var_304_begin_0 = const()[name = tensor("op_304_begin_0"), val = tensor([176])]; + tensor var_304_end_0 = const()[name = tensor("op_304_end_0"), val = tensor([765])]; + tensor var_304_end_mask_0 = const()[name = tensor("op_304_end_mask_0"), val = tensor([false])]; + tensor var_304_cast_fp16 = slice_by_index(begin = var_304_begin_0, end = var_304_end_0, end_mask = var_304_end_mask_0, x = scatter_4_cast_fp16)[name = tensor("op_304_cast_fp16")]; + tensor var_307_begin_0 = const()[name = tensor("op_307_begin_0"), val = tensor([3, 0])]; + tensor var_307_end_0 = const()[name = tensor("op_307_end_0"), val = tensor([4, 589])]; + tensor var_307_end_mask_0 = const()[name = tensor("op_307_end_mask_0"), val = tensor([false, true])]; + tensor var_307_squeeze_mask_0 = const()[name = tensor("op_307_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_307_cast_fp16 = slice_by_index(begin = var_307_begin_0, end = var_307_end_0, end_mask = var_307_end_mask_0, squeeze_mask = var_307_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_307_cast_fp16")]; + tensor var_309_cast_fp16 = add(x = var_304_cast_fp16, y = var_307_cast_fp16)[name = tensor("op_309_cast_fp16")]; + tensor slice_by_index_6 = const()[name = tensor("slice_by_index_6"), val = tensor([176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764])]; + tensor scatter_6_mode_0 = const()[name = tensor("scatter_6_mode_0"), val = tensor("update")]; + tensor scatter_6_axis_0 = const()[name = tensor("scatter_6_axis_0"), val = tensor(0)]; + tensor scatter_6_cast_fp16 = scatter(axis = scatter_6_axis_0, data = scatter_4_cast_fp16, indices = slice_by_index_6, mode = scatter_6_mode_0, updates = var_309_cast_fp16)[name = tensor("scatter_6_cast_fp16")]; + tensor var_319_begin_0 = const()[name = tensor("op_319_begin_0"), val = tensor([176])]; + tensor var_319_end_0 = const()[name = tensor("op_319_end_0"), val = tensor([765])]; + tensor var_319_end_mask_0 = const()[name = tensor("op_319_end_mask_0"), val = tensor([false])]; + tensor var_319_cast_fp16 = slice_by_index(begin = var_319_begin_0, end = var_319_end_0, end_mask = var_319_end_mask_0, x = scatter_5_cast_fp16)[name = tensor("op_319_cast_fp16")]; + tensor var_320_to_fp16 = const()[name = tensor("op_320_to_fp16"), val = tensor(0x1p+0)]; + tensor var_322_cast_fp16 = add(x = var_319_cast_fp16, y = var_320_to_fp16)[name = tensor("op_322_cast_fp16")]; + tensor scatter_7_mode_0 = const()[name = tensor("scatter_7_mode_0"), val = tensor("update")]; + tensor scatter_7_axis_0 = const()[name = tensor("scatter_7_axis_0"), val = tensor(0)]; + tensor scatter_7_cast_fp16 = scatter(axis = scatter_7_axis_0, data = scatter_5_cast_fp16, indices = slice_by_index_6, mode = scatter_7_mode_0, updates = var_322_cast_fp16)[name = tensor("scatter_7_cast_fp16")]; + tensor var_339_begin_0 = const()[name = tensor("op_339_begin_0"), val = tensor([235])]; + tensor var_339_end_0 = const()[name = tensor("op_339_end_0"), val = tensor([824])]; + tensor var_339_end_mask_0 = const()[name = tensor("op_339_end_mask_0"), val = tensor([false])]; + tensor var_339_cast_fp16 = slice_by_index(begin = var_339_begin_0, end = var_339_end_0, end_mask = var_339_end_mask_0, x = scatter_6_cast_fp16)[name = tensor("op_339_cast_fp16")]; + tensor var_342_begin_0 = const()[name = tensor("op_342_begin_0"), val = tensor([4, 0])]; + tensor var_342_end_0 = const()[name = tensor("op_342_end_0"), val = tensor([5, 589])]; + tensor var_342_end_mask_0 = const()[name = tensor("op_342_end_mask_0"), val = tensor([false, true])]; + tensor var_342_squeeze_mask_0 = const()[name = tensor("op_342_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_342_cast_fp16 = slice_by_index(begin = var_342_begin_0, end = var_342_end_0, end_mask = var_342_end_mask_0, squeeze_mask = var_342_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_342_cast_fp16")]; + tensor var_344_cast_fp16 = add(x = var_339_cast_fp16, y = var_342_cast_fp16)[name = tensor("op_344_cast_fp16")]; + tensor slice_by_index_8 = const()[name = tensor("slice_by_index_8"), val = tensor([235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823])]; + tensor scatter_8_mode_0 = const()[name = tensor("scatter_8_mode_0"), val = tensor("update")]; + tensor scatter_8_axis_0 = const()[name = tensor("scatter_8_axis_0"), val = tensor(0)]; + tensor scatter_8_cast_fp16 = scatter(axis = scatter_8_axis_0, data = scatter_6_cast_fp16, indices = slice_by_index_8, mode = scatter_8_mode_0, updates = var_344_cast_fp16)[name = tensor("scatter_8_cast_fp16")]; + tensor var_354_begin_0 = const()[name = tensor("op_354_begin_0"), val = tensor([235])]; + tensor var_354_end_0 = const()[name = tensor("op_354_end_0"), val = tensor([824])]; + tensor var_354_end_mask_0 = const()[name = tensor("op_354_end_mask_0"), val = tensor([false])]; + tensor var_354_cast_fp16 = slice_by_index(begin = var_354_begin_0, end = var_354_end_0, end_mask = var_354_end_mask_0, x = scatter_7_cast_fp16)[name = tensor("op_354_cast_fp16")]; + tensor var_355_to_fp16 = const()[name = tensor("op_355_to_fp16"), val = tensor(0x1p+0)]; + tensor var_357_cast_fp16 = add(x = var_354_cast_fp16, y = var_355_to_fp16)[name = tensor("op_357_cast_fp16")]; + tensor scatter_9_mode_0 = const()[name = tensor("scatter_9_mode_0"), val = tensor("update")]; + tensor scatter_9_axis_0 = const()[name = tensor("scatter_9_axis_0"), val = tensor(0)]; + tensor scatter_9_cast_fp16 = scatter(axis = scatter_9_axis_0, data = scatter_7_cast_fp16, indices = slice_by_index_8, mode = scatter_9_mode_0, updates = var_357_cast_fp16)[name = tensor("scatter_9_cast_fp16")]; + tensor var_374_begin_0 = const()[name = tensor("op_374_begin_0"), val = tensor([294])]; + tensor var_374_end_0 = const()[name = tensor("op_374_end_0"), val = tensor([883])]; + tensor var_374_end_mask_0 = const()[name = tensor("op_374_end_mask_0"), val = tensor([false])]; + tensor var_374_cast_fp16 = slice_by_index(begin = var_374_begin_0, end = var_374_end_0, end_mask = var_374_end_mask_0, x = scatter_8_cast_fp16)[name = tensor("op_374_cast_fp16")]; + tensor var_377_begin_0 = const()[name = tensor("op_377_begin_0"), val = tensor([5, 0])]; + tensor var_377_end_0 = const()[name = tensor("op_377_end_0"), val = tensor([6, 589])]; + tensor var_377_end_mask_0 = const()[name = tensor("op_377_end_mask_0"), val = tensor([false, true])]; + tensor var_377_squeeze_mask_0 = const()[name = tensor("op_377_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_377_cast_fp16 = slice_by_index(begin = var_377_begin_0, end = var_377_end_0, end_mask = var_377_end_mask_0, squeeze_mask = var_377_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_377_cast_fp16")]; + tensor var_379_cast_fp16 = add(x = var_374_cast_fp16, y = var_377_cast_fp16)[name = tensor("op_379_cast_fp16")]; + tensor slice_by_index_10 = const()[name = tensor("slice_by_index_10"), val = tensor([294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882])]; + tensor scatter_10_mode_0 = const()[name = tensor("scatter_10_mode_0"), val = tensor("update")]; + tensor scatter_10_axis_0 = const()[name = tensor("scatter_10_axis_0"), val = tensor(0)]; + tensor scatter_10_cast_fp16 = scatter(axis = scatter_10_axis_0, data = scatter_8_cast_fp16, indices = slice_by_index_10, mode = scatter_10_mode_0, updates = var_379_cast_fp16)[name = tensor("scatter_10_cast_fp16")]; + tensor var_389_begin_0 = const()[name = tensor("op_389_begin_0"), val = tensor([294])]; + tensor var_389_end_0 = const()[name = tensor("op_389_end_0"), val = tensor([883])]; + tensor var_389_end_mask_0 = const()[name = tensor("op_389_end_mask_0"), val = tensor([false])]; + tensor var_389_cast_fp16 = slice_by_index(begin = var_389_begin_0, end = var_389_end_0, end_mask = var_389_end_mask_0, x = scatter_9_cast_fp16)[name = tensor("op_389_cast_fp16")]; + tensor var_390_to_fp16 = const()[name = tensor("op_390_to_fp16"), val = tensor(0x1p+0)]; + tensor var_392_cast_fp16 = add(x = var_389_cast_fp16, y = var_390_to_fp16)[name = tensor("op_392_cast_fp16")]; + tensor scatter_11_mode_0 = const()[name = tensor("scatter_11_mode_0"), val = tensor("update")]; + tensor scatter_11_axis_0 = const()[name = tensor("scatter_11_axis_0"), val = tensor(0)]; + tensor scatter_11_cast_fp16 = scatter(axis = scatter_11_axis_0, data = scatter_9_cast_fp16, indices = slice_by_index_10, mode = scatter_11_mode_0, updates = var_392_cast_fp16)[name = tensor("scatter_11_cast_fp16")]; + tensor var_409_begin_0 = const()[name = tensor("op_409_begin_0"), val = tensor([353])]; + tensor var_409_end_0 = const()[name = tensor("op_409_end_0"), val = tensor([942])]; + tensor var_409_end_mask_0 = const()[name = tensor("op_409_end_mask_0"), val = tensor([false])]; + tensor var_409_cast_fp16 = slice_by_index(begin = var_409_begin_0, end = var_409_end_0, end_mask = var_409_end_mask_0, x = scatter_10_cast_fp16)[name = tensor("op_409_cast_fp16")]; + tensor var_412_begin_0 = const()[name = tensor("op_412_begin_0"), val = tensor([6, 0])]; + tensor var_412_end_0 = const()[name = tensor("op_412_end_0"), val = tensor([7, 589])]; + tensor var_412_end_mask_0 = const()[name = tensor("op_412_end_mask_0"), val = tensor([false, true])]; + tensor var_412_squeeze_mask_0 = const()[name = tensor("op_412_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_412_cast_fp16 = slice_by_index(begin = var_412_begin_0, end = var_412_end_0, end_mask = var_412_end_mask_0, squeeze_mask = var_412_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_412_cast_fp16")]; + tensor var_414_cast_fp16 = add(x = var_409_cast_fp16, y = var_412_cast_fp16)[name = tensor("op_414_cast_fp16")]; + tensor slice_by_index_12 = const()[name = tensor("slice_by_index_12"), val = tensor([353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941])]; + tensor scatter_12_mode_0 = const()[name = tensor("scatter_12_mode_0"), val = tensor("update")]; + tensor scatter_12_axis_0 = const()[name = tensor("scatter_12_axis_0"), val = tensor(0)]; + tensor scatter_12_cast_fp16 = scatter(axis = scatter_12_axis_0, data = scatter_10_cast_fp16, indices = slice_by_index_12, mode = scatter_12_mode_0, updates = var_414_cast_fp16)[name = tensor("scatter_12_cast_fp16")]; + tensor var_424_begin_0 = const()[name = tensor("op_424_begin_0"), val = tensor([353])]; + tensor var_424_end_0 = const()[name = tensor("op_424_end_0"), val = tensor([942])]; + tensor var_424_end_mask_0 = const()[name = tensor("op_424_end_mask_0"), val = tensor([false])]; + tensor var_424_cast_fp16 = slice_by_index(begin = var_424_begin_0, end = var_424_end_0, end_mask = var_424_end_mask_0, x = scatter_11_cast_fp16)[name = tensor("op_424_cast_fp16")]; + tensor var_425_to_fp16 = const()[name = tensor("op_425_to_fp16"), val = tensor(0x1p+0)]; + tensor var_427_cast_fp16 = add(x = var_424_cast_fp16, y = var_425_to_fp16)[name = tensor("op_427_cast_fp16")]; + tensor scatter_13_mode_0 = const()[name = tensor("scatter_13_mode_0"), val = tensor("update")]; + tensor scatter_13_axis_0 = const()[name = tensor("scatter_13_axis_0"), val = tensor(0)]; + tensor scatter_13_cast_fp16 = scatter(axis = scatter_13_axis_0, data = scatter_11_cast_fp16, indices = slice_by_index_12, mode = scatter_13_mode_0, updates = var_427_cast_fp16)[name = tensor("scatter_13_cast_fp16")]; + tensor var_444_begin_0 = const()[name = tensor("op_444_begin_0"), val = tensor([412])]; + tensor var_444_end_0 = const()[name = tensor("op_444_end_0"), val = tensor([1001])]; + tensor var_444_end_mask_0 = const()[name = tensor("op_444_end_mask_0"), val = tensor([false])]; + tensor var_444_cast_fp16 = slice_by_index(begin = var_444_begin_0, end = var_444_end_0, end_mask = var_444_end_mask_0, x = scatter_12_cast_fp16)[name = tensor("op_444_cast_fp16")]; + tensor var_447_begin_0 = const()[name = tensor("op_447_begin_0"), val = tensor([7, 0])]; + tensor var_447_end_0 = const()[name = tensor("op_447_end_0"), val = tensor([8, 589])]; + tensor var_447_end_mask_0 = const()[name = tensor("op_447_end_mask_0"), val = tensor([false, true])]; + tensor var_447_squeeze_mask_0 = const()[name = tensor("op_447_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_447_cast_fp16 = slice_by_index(begin = var_447_begin_0, end = var_447_end_0, end_mask = var_447_end_mask_0, squeeze_mask = var_447_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_447_cast_fp16")]; + tensor var_449_cast_fp16 = add(x = var_444_cast_fp16, y = var_447_cast_fp16)[name = tensor("op_449_cast_fp16")]; + tensor slice_by_index_14 = const()[name = tensor("slice_by_index_14"), val = tensor([412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000])]; + tensor scatter_14_mode_0 = const()[name = tensor("scatter_14_mode_0"), val = tensor("update")]; + tensor scatter_14_axis_0 = const()[name = tensor("scatter_14_axis_0"), val = tensor(0)]; + tensor scatter_14_cast_fp16 = scatter(axis = scatter_14_axis_0, data = scatter_12_cast_fp16, indices = slice_by_index_14, mode = scatter_14_mode_0, updates = var_449_cast_fp16)[name = tensor("scatter_14_cast_fp16")]; + tensor var_459_begin_0 = const()[name = tensor("op_459_begin_0"), val = tensor([412])]; + tensor var_459_end_0 = const()[name = tensor("op_459_end_0"), val = tensor([1001])]; + tensor var_459_end_mask_0 = const()[name = tensor("op_459_end_mask_0"), val = tensor([false])]; + tensor var_459_cast_fp16 = slice_by_index(begin = var_459_begin_0, end = var_459_end_0, end_mask = var_459_end_mask_0, x = scatter_13_cast_fp16)[name = tensor("op_459_cast_fp16")]; + tensor var_460_to_fp16 = const()[name = tensor("op_460_to_fp16"), val = tensor(0x1p+0)]; + tensor var_462_cast_fp16 = add(x = var_459_cast_fp16, y = var_460_to_fp16)[name = tensor("op_462_cast_fp16")]; + tensor scatter_15_mode_0 = const()[name = tensor("scatter_15_mode_0"), val = tensor("update")]; + tensor scatter_15_axis_0 = const()[name = tensor("scatter_15_axis_0"), val = tensor(0)]; + tensor scatter_15_cast_fp16 = scatter(axis = scatter_15_axis_0, data = scatter_13_cast_fp16, indices = slice_by_index_14, mode = scatter_15_mode_0, updates = var_462_cast_fp16)[name = tensor("scatter_15_cast_fp16")]; + tensor var_479_begin_0 = const()[name = tensor("op_479_begin_0"), val = tensor([471])]; + tensor var_479_end_0 = const()[name = tensor("op_479_end_0"), val = tensor([1060])]; + tensor var_479_end_mask_0 = const()[name = tensor("op_479_end_mask_0"), val = tensor([false])]; + tensor var_479_cast_fp16 = slice_by_index(begin = var_479_begin_0, end = var_479_end_0, end_mask = var_479_end_mask_0, x = scatter_14_cast_fp16)[name = tensor("op_479_cast_fp16")]; + tensor var_482_begin_0 = const()[name = tensor("op_482_begin_0"), val = tensor([8, 0])]; + tensor var_482_end_0 = const()[name = tensor("op_482_end_0"), val = tensor([9, 589])]; + tensor var_482_end_mask_0 = const()[name = tensor("op_482_end_mask_0"), val = tensor([false, true])]; + tensor var_482_squeeze_mask_0 = const()[name = tensor("op_482_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_482_cast_fp16 = slice_by_index(begin = var_482_begin_0, end = var_482_end_0, end_mask = var_482_end_mask_0, squeeze_mask = var_482_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_482_cast_fp16")]; + tensor var_484_cast_fp16 = add(x = var_479_cast_fp16, y = var_482_cast_fp16)[name = tensor("op_484_cast_fp16")]; + tensor slice_by_index_16 = const()[name = tensor("slice_by_index_16"), val = tensor([471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059])]; + tensor scatter_16_mode_0 = const()[name = tensor("scatter_16_mode_0"), val = tensor("update")]; + tensor scatter_16_axis_0 = const()[name = tensor("scatter_16_axis_0"), val = tensor(0)]; + tensor scatter_16_cast_fp16 = scatter(axis = scatter_16_axis_0, data = scatter_14_cast_fp16, indices = slice_by_index_16, mode = scatter_16_mode_0, updates = var_484_cast_fp16)[name = tensor("scatter_16_cast_fp16")]; + tensor var_494_begin_0 = const()[name = tensor("op_494_begin_0"), val = tensor([471])]; + tensor var_494_end_0 = const()[name = tensor("op_494_end_0"), val = tensor([1060])]; + tensor var_494_end_mask_0 = const()[name = tensor("op_494_end_mask_0"), val = tensor([false])]; + tensor var_494_cast_fp16 = slice_by_index(begin = var_494_begin_0, end = var_494_end_0, end_mask = var_494_end_mask_0, x = scatter_15_cast_fp16)[name = tensor("op_494_cast_fp16")]; + tensor var_495_to_fp16 = const()[name = tensor("op_495_to_fp16"), val = tensor(0x1p+0)]; + tensor var_497_cast_fp16 = add(x = var_494_cast_fp16, y = var_495_to_fp16)[name = tensor("op_497_cast_fp16")]; + tensor scatter_17_mode_0 = const()[name = tensor("scatter_17_mode_0"), val = tensor("update")]; + tensor scatter_17_axis_0 = const()[name = tensor("scatter_17_axis_0"), val = tensor(0)]; + tensor scatter_17_cast_fp16 = scatter(axis = scatter_17_axis_0, data = scatter_15_cast_fp16, indices = slice_by_index_16, mode = scatter_17_mode_0, updates = var_497_cast_fp16)[name = tensor("scatter_17_cast_fp16")]; + tensor var_514_begin_0 = const()[name = tensor("op_514_begin_0"), val = tensor([530])]; + tensor var_514_end_0 = const()[name = tensor("op_514_end_0"), val = tensor([1119])]; + tensor var_514_end_mask_0 = const()[name = tensor("op_514_end_mask_0"), val = tensor([false])]; + tensor var_514_cast_fp16 = slice_by_index(begin = var_514_begin_0, end = var_514_end_0, end_mask = var_514_end_mask_0, x = scatter_16_cast_fp16)[name = tensor("op_514_cast_fp16")]; + tensor var_517_begin_0 = const()[name = tensor("op_517_begin_0"), val = tensor([9, 0])]; + tensor var_517_end_0 = const()[name = tensor("op_517_end_0"), val = tensor([10, 589])]; + tensor var_517_end_mask_0 = const()[name = tensor("op_517_end_mask_0"), val = tensor([false, true])]; + tensor var_517_squeeze_mask_0 = const()[name = tensor("op_517_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_517_cast_fp16 = slice_by_index(begin = var_517_begin_0, end = var_517_end_0, end_mask = var_517_end_mask_0, squeeze_mask = var_517_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_517_cast_fp16")]; + tensor var_519_cast_fp16 = add(x = var_514_cast_fp16, y = var_517_cast_fp16)[name = tensor("op_519_cast_fp16")]; + tensor slice_by_index_18 = const()[name = tensor("slice_by_index_18"), val = tensor([530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118])]; + tensor scatter_18_mode_0 = const()[name = tensor("scatter_18_mode_0"), val = tensor("update")]; + tensor scatter_18_axis_0 = const()[name = tensor("scatter_18_axis_0"), val = tensor(0)]; + tensor scatter_18_cast_fp16 = scatter(axis = scatter_18_axis_0, data = scatter_16_cast_fp16, indices = slice_by_index_18, mode = scatter_18_mode_0, updates = var_519_cast_fp16)[name = tensor("scatter_18_cast_fp16")]; + tensor var_529_begin_0 = const()[name = tensor("op_529_begin_0"), val = tensor([530])]; + tensor var_529_end_0 = const()[name = tensor("op_529_end_0"), val = tensor([1119])]; + tensor var_529_end_mask_0 = const()[name = tensor("op_529_end_mask_0"), val = tensor([false])]; + tensor var_529_cast_fp16 = slice_by_index(begin = var_529_begin_0, end = var_529_end_0, end_mask = var_529_end_mask_0, x = scatter_17_cast_fp16)[name = tensor("op_529_cast_fp16")]; + tensor var_530_to_fp16 = const()[name = tensor("op_530_to_fp16"), val = tensor(0x1p+0)]; + tensor var_532_cast_fp16 = add(x = var_529_cast_fp16, y = var_530_to_fp16)[name = tensor("op_532_cast_fp16")]; + tensor scatter_19_mode_0 = const()[name = tensor("scatter_19_mode_0"), val = tensor("update")]; + tensor scatter_19_axis_0 = const()[name = tensor("scatter_19_axis_0"), val = tensor(0)]; + tensor scatter_19_cast_fp16 = scatter(axis = scatter_19_axis_0, data = scatter_17_cast_fp16, indices = slice_by_index_18, mode = scatter_19_mode_0, updates = var_532_cast_fp16)[name = tensor("scatter_19_cast_fp16")]; + tensor var_549_begin_0 = const()[name = tensor("op_549_begin_0"), val = tensor([589])]; + tensor var_549_end_0 = const()[name = tensor("op_549_end_0"), val = tensor([1178])]; + tensor var_549_end_mask_0 = const()[name = tensor("op_549_end_mask_0"), val = tensor([false])]; + tensor var_549_cast_fp16 = slice_by_index(begin = var_549_begin_0, end = var_549_end_0, end_mask = var_549_end_mask_0, x = scatter_18_cast_fp16)[name = tensor("op_549_cast_fp16")]; + tensor var_552_begin_0 = const()[name = tensor("op_552_begin_0"), val = tensor([10, 0])]; + tensor var_552_end_0 = const()[name = tensor("op_552_end_0"), val = tensor([11, 589])]; + tensor var_552_end_mask_0 = const()[name = tensor("op_552_end_mask_0"), val = tensor([false, true])]; + tensor var_552_squeeze_mask_0 = const()[name = tensor("op_552_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_552_cast_fp16 = slice_by_index(begin = var_552_begin_0, end = var_552_end_0, end_mask = var_552_end_mask_0, squeeze_mask = var_552_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_552_cast_fp16")]; + tensor var_554_cast_fp16 = add(x = var_549_cast_fp16, y = var_552_cast_fp16)[name = tensor("op_554_cast_fp16")]; + tensor slice_by_index_20 = const()[name = tensor("slice_by_index_20"), val = tensor([589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177])]; + tensor scatter_20_mode_0 = const()[name = tensor("scatter_20_mode_0"), val = tensor("update")]; + tensor scatter_20_axis_0 = const()[name = tensor("scatter_20_axis_0"), val = tensor(0)]; + tensor scatter_20_cast_fp16 = scatter(axis = scatter_20_axis_0, data = scatter_18_cast_fp16, indices = slice_by_index_20, mode = scatter_20_mode_0, updates = var_554_cast_fp16)[name = tensor("scatter_20_cast_fp16")]; + tensor var_564_begin_0 = const()[name = tensor("op_564_begin_0"), val = tensor([589])]; + tensor var_564_end_0 = const()[name = tensor("op_564_end_0"), val = tensor([1178])]; + tensor var_564_end_mask_0 = const()[name = tensor("op_564_end_mask_0"), val = tensor([false])]; + tensor var_564_cast_fp16 = slice_by_index(begin = var_564_begin_0, end = var_564_end_0, end_mask = var_564_end_mask_0, x = scatter_19_cast_fp16)[name = tensor("op_564_cast_fp16")]; + tensor var_565_to_fp16 = const()[name = tensor("op_565_to_fp16"), val = tensor(0x1p+0)]; + tensor var_567_cast_fp16 = add(x = var_564_cast_fp16, y = var_565_to_fp16)[name = tensor("op_567_cast_fp16")]; + tensor scatter_21_mode_0 = const()[name = tensor("scatter_21_mode_0"), val = tensor("update")]; + tensor scatter_21_axis_0 = const()[name = tensor("scatter_21_axis_0"), val = tensor(0)]; + tensor scatter_21_cast_fp16 = scatter(axis = scatter_21_axis_0, data = scatter_19_cast_fp16, indices = slice_by_index_20, mode = scatter_21_mode_0, updates = var_567_cast_fp16)[name = tensor("scatter_21_cast_fp16")]; + tensor var_584_begin_0 = const()[name = tensor("op_584_begin_0"), val = tensor([647])]; + tensor var_584_end_0 = const()[name = tensor("op_584_end_0"), val = tensor([1236])]; + tensor var_584_end_mask_0 = const()[name = tensor("op_584_end_mask_0"), val = tensor([false])]; + tensor var_584_cast_fp16 = slice_by_index(begin = var_584_begin_0, end = var_584_end_0, end_mask = var_584_end_mask_0, x = scatter_20_cast_fp16)[name = tensor("op_584_cast_fp16")]; + tensor var_587_begin_0 = const()[name = tensor("op_587_begin_0"), val = tensor([11, 0])]; + tensor var_587_end_0 = const()[name = tensor("op_587_end_0"), val = tensor([12, 589])]; + tensor var_587_end_mask_0 = const()[name = tensor("op_587_end_mask_0"), val = tensor([false, true])]; + tensor var_587_squeeze_mask_0 = const()[name = tensor("op_587_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_587_cast_fp16 = slice_by_index(begin = var_587_begin_0, end = var_587_end_0, end_mask = var_587_end_mask_0, squeeze_mask = var_587_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_587_cast_fp16")]; + tensor var_589_cast_fp16 = add(x = var_584_cast_fp16, y = var_587_cast_fp16)[name = tensor("op_589_cast_fp16")]; + tensor slice_by_index_22 = const()[name = tensor("slice_by_index_22"), val = tensor([647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235])]; + tensor scatter_22_mode_0 = const()[name = tensor("scatter_22_mode_0"), val = tensor("update")]; + tensor scatter_22_axis_0 = const()[name = tensor("scatter_22_axis_0"), val = tensor(0)]; + tensor scatter_22_cast_fp16 = scatter(axis = scatter_22_axis_0, data = scatter_20_cast_fp16, indices = slice_by_index_22, mode = scatter_22_mode_0, updates = var_589_cast_fp16)[name = tensor("scatter_22_cast_fp16")]; + tensor var_599_begin_0 = const()[name = tensor("op_599_begin_0"), val = tensor([647])]; + tensor var_599_end_0 = const()[name = tensor("op_599_end_0"), val = tensor([1236])]; + tensor var_599_end_mask_0 = const()[name = tensor("op_599_end_mask_0"), val = tensor([false])]; + tensor var_599_cast_fp16 = slice_by_index(begin = var_599_begin_0, end = var_599_end_0, end_mask = var_599_end_mask_0, x = scatter_21_cast_fp16)[name = tensor("op_599_cast_fp16")]; + tensor var_600_to_fp16 = const()[name = tensor("op_600_to_fp16"), val = tensor(0x1p+0)]; + tensor var_602_cast_fp16 = add(x = var_599_cast_fp16, y = var_600_to_fp16)[name = tensor("op_602_cast_fp16")]; + tensor scatter_23_mode_0 = const()[name = tensor("scatter_23_mode_0"), val = tensor("update")]; + tensor scatter_23_axis_0 = const()[name = tensor("scatter_23_axis_0"), val = tensor(0)]; + tensor scatter_23_cast_fp16 = scatter(axis = scatter_23_axis_0, data = scatter_21_cast_fp16, indices = slice_by_index_22, mode = scatter_23_mode_0, updates = var_602_cast_fp16)[name = tensor("scatter_23_cast_fp16")]; + tensor var_619_begin_0 = const()[name = tensor("op_619_begin_0"), val = tensor([706])]; + tensor var_619_end_0 = const()[name = tensor("op_619_end_0"), val = tensor([1295])]; + tensor var_619_end_mask_0 = const()[name = tensor("op_619_end_mask_0"), val = tensor([false])]; + tensor var_619_cast_fp16 = slice_by_index(begin = var_619_begin_0, end = var_619_end_0, end_mask = var_619_end_mask_0, x = scatter_22_cast_fp16)[name = tensor("op_619_cast_fp16")]; + tensor var_622_begin_0 = const()[name = tensor("op_622_begin_0"), val = tensor([12, 0])]; + tensor var_622_end_0 = const()[name = tensor("op_622_end_0"), val = tensor([13, 589])]; + tensor var_622_end_mask_0 = const()[name = tensor("op_622_end_mask_0"), val = tensor([false, true])]; + tensor var_622_squeeze_mask_0 = const()[name = tensor("op_622_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_622_cast_fp16 = slice_by_index(begin = var_622_begin_0, end = var_622_end_0, end_mask = var_622_end_mask_0, squeeze_mask = var_622_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_622_cast_fp16")]; + tensor var_624_cast_fp16 = add(x = var_619_cast_fp16, y = var_622_cast_fp16)[name = tensor("op_624_cast_fp16")]; + tensor slice_by_index_24 = const()[name = tensor("slice_by_index_24"), val = tensor([706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294])]; + tensor scatter_24_mode_0 = const()[name = tensor("scatter_24_mode_0"), val = tensor("update")]; + tensor scatter_24_axis_0 = const()[name = tensor("scatter_24_axis_0"), val = tensor(0)]; + tensor scatter_24_cast_fp16 = scatter(axis = scatter_24_axis_0, data = scatter_22_cast_fp16, indices = slice_by_index_24, mode = scatter_24_mode_0, updates = var_624_cast_fp16)[name = tensor("scatter_24_cast_fp16")]; + tensor var_634_begin_0 = const()[name = tensor("op_634_begin_0"), val = tensor([706])]; + tensor var_634_end_0 = const()[name = tensor("op_634_end_0"), val = tensor([1295])]; + tensor var_634_end_mask_0 = const()[name = tensor("op_634_end_mask_0"), val = tensor([false])]; + tensor var_634_cast_fp16 = slice_by_index(begin = var_634_begin_0, end = var_634_end_0, end_mask = var_634_end_mask_0, x = scatter_23_cast_fp16)[name = tensor("op_634_cast_fp16")]; + tensor var_635_to_fp16 = const()[name = tensor("op_635_to_fp16"), val = tensor(0x1p+0)]; + tensor var_637_cast_fp16 = add(x = var_634_cast_fp16, y = var_635_to_fp16)[name = tensor("op_637_cast_fp16")]; + tensor scatter_25_mode_0 = const()[name = tensor("scatter_25_mode_0"), val = tensor("update")]; + tensor scatter_25_axis_0 = const()[name = tensor("scatter_25_axis_0"), val = tensor(0)]; + tensor scatter_25_cast_fp16 = scatter(axis = scatter_25_axis_0, data = scatter_23_cast_fp16, indices = slice_by_index_24, mode = scatter_25_mode_0, updates = var_637_cast_fp16)[name = tensor("scatter_25_cast_fp16")]; + tensor var_654_begin_0 = const()[name = tensor("op_654_begin_0"), val = tensor([765])]; + tensor var_654_end_0 = const()[name = tensor("op_654_end_0"), val = tensor([1354])]; + tensor var_654_end_mask_0 = const()[name = tensor("op_654_end_mask_0"), val = tensor([false])]; + tensor var_654_cast_fp16 = slice_by_index(begin = var_654_begin_0, end = var_654_end_0, end_mask = var_654_end_mask_0, x = scatter_24_cast_fp16)[name = tensor("op_654_cast_fp16")]; + tensor var_657_begin_0 = const()[name = tensor("op_657_begin_0"), val = tensor([13, 0])]; + tensor var_657_end_0 = const()[name = tensor("op_657_end_0"), val = tensor([14, 589])]; + tensor var_657_end_mask_0 = const()[name = tensor("op_657_end_mask_0"), val = tensor([false, true])]; + tensor var_657_squeeze_mask_0 = const()[name = tensor("op_657_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_657_cast_fp16 = slice_by_index(begin = var_657_begin_0, end = var_657_end_0, end_mask = var_657_end_mask_0, squeeze_mask = var_657_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_657_cast_fp16")]; + tensor var_659_cast_fp16 = add(x = var_654_cast_fp16, y = var_657_cast_fp16)[name = tensor("op_659_cast_fp16")]; + tensor slice_by_index_26 = const()[name = tensor("slice_by_index_26"), val = tensor([765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353])]; + tensor scatter_26_mode_0 = const()[name = tensor("scatter_26_mode_0"), val = tensor("update")]; + tensor scatter_26_axis_0 = const()[name = tensor("scatter_26_axis_0"), val = tensor(0)]; + tensor scatter_26_cast_fp16 = scatter(axis = scatter_26_axis_0, data = scatter_24_cast_fp16, indices = slice_by_index_26, mode = scatter_26_mode_0, updates = var_659_cast_fp16)[name = tensor("scatter_26_cast_fp16")]; + tensor var_669_begin_0 = const()[name = tensor("op_669_begin_0"), val = tensor([765])]; + tensor var_669_end_0 = const()[name = tensor("op_669_end_0"), val = tensor([1354])]; + tensor var_669_end_mask_0 = const()[name = tensor("op_669_end_mask_0"), val = tensor([false])]; + tensor var_669_cast_fp16 = slice_by_index(begin = var_669_begin_0, end = var_669_end_0, end_mask = var_669_end_mask_0, x = scatter_25_cast_fp16)[name = tensor("op_669_cast_fp16")]; + tensor var_670_to_fp16 = const()[name = tensor("op_670_to_fp16"), val = tensor(0x1p+0)]; + tensor var_672_cast_fp16 = add(x = var_669_cast_fp16, y = var_670_to_fp16)[name = tensor("op_672_cast_fp16")]; + tensor scatter_27_mode_0 = const()[name = tensor("scatter_27_mode_0"), val = tensor("update")]; + tensor scatter_27_axis_0 = const()[name = tensor("scatter_27_axis_0"), val = tensor(0)]; + tensor scatter_27_cast_fp16 = scatter(axis = scatter_27_axis_0, data = scatter_25_cast_fp16, indices = slice_by_index_26, mode = scatter_27_mode_0, updates = var_672_cast_fp16)[name = tensor("scatter_27_cast_fp16")]; + tensor var_689_begin_0 = const()[name = tensor("op_689_begin_0"), val = tensor([824])]; + tensor var_689_end_0 = const()[name = tensor("op_689_end_0"), val = tensor([1413])]; + tensor var_689_end_mask_0 = const()[name = tensor("op_689_end_mask_0"), val = tensor([false])]; + tensor var_689_cast_fp16 = slice_by_index(begin = var_689_begin_0, end = var_689_end_0, end_mask = var_689_end_mask_0, x = scatter_26_cast_fp16)[name = tensor("op_689_cast_fp16")]; + tensor var_692_begin_0 = const()[name = tensor("op_692_begin_0"), val = tensor([14, 0])]; + tensor var_692_end_0 = const()[name = tensor("op_692_end_0"), val = tensor([15, 589])]; + tensor var_692_end_mask_0 = const()[name = tensor("op_692_end_mask_0"), val = tensor([false, true])]; + tensor var_692_squeeze_mask_0 = const()[name = tensor("op_692_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_692_cast_fp16 = slice_by_index(begin = var_692_begin_0, end = var_692_end_0, end_mask = var_692_end_mask_0, squeeze_mask = var_692_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_692_cast_fp16")]; + tensor var_694_cast_fp16 = add(x = var_689_cast_fp16, y = var_692_cast_fp16)[name = tensor("op_694_cast_fp16")]; + tensor slice_by_index_28 = const()[name = tensor("slice_by_index_28"), val = tensor([824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412])]; + tensor scatter_28_mode_0 = const()[name = tensor("scatter_28_mode_0"), val = tensor("update")]; + tensor scatter_28_axis_0 = const()[name = tensor("scatter_28_axis_0"), val = tensor(0)]; + tensor scatter_28_cast_fp16 = scatter(axis = scatter_28_axis_0, data = scatter_26_cast_fp16, indices = slice_by_index_28, mode = scatter_28_mode_0, updates = var_694_cast_fp16)[name = tensor("scatter_28_cast_fp16")]; + tensor var_704_begin_0 = const()[name = tensor("op_704_begin_0"), val = tensor([824])]; + tensor var_704_end_0 = const()[name = tensor("op_704_end_0"), val = tensor([1413])]; + tensor var_704_end_mask_0 = const()[name = tensor("op_704_end_mask_0"), val = tensor([false])]; + tensor var_704_cast_fp16 = slice_by_index(begin = var_704_begin_0, end = var_704_end_0, end_mask = var_704_end_mask_0, x = scatter_27_cast_fp16)[name = tensor("op_704_cast_fp16")]; + tensor var_705_to_fp16 = const()[name = tensor("op_705_to_fp16"), val = tensor(0x1p+0)]; + tensor var_707_cast_fp16 = add(x = var_704_cast_fp16, y = var_705_to_fp16)[name = tensor("op_707_cast_fp16")]; + tensor scatter_29_mode_0 = const()[name = tensor("scatter_29_mode_0"), val = tensor("update")]; + tensor scatter_29_axis_0 = const()[name = tensor("scatter_29_axis_0"), val = tensor(0)]; + tensor scatter_29_cast_fp16 = scatter(axis = scatter_29_axis_0, data = scatter_27_cast_fp16, indices = slice_by_index_28, mode = scatter_29_mode_0, updates = var_707_cast_fp16)[name = tensor("scatter_29_cast_fp16")]; + tensor var_724_begin_0 = const()[name = tensor("op_724_begin_0"), val = tensor([883])]; + tensor var_724_end_0 = const()[name = tensor("op_724_end_0"), val = tensor([1472])]; + tensor var_724_end_mask_0 = const()[name = tensor("op_724_end_mask_0"), val = tensor([false])]; + tensor var_724_cast_fp16 = slice_by_index(begin = var_724_begin_0, end = var_724_end_0, end_mask = var_724_end_mask_0, x = scatter_28_cast_fp16)[name = tensor("op_724_cast_fp16")]; + tensor var_727_begin_0 = const()[name = tensor("op_727_begin_0"), val = tensor([15, 0])]; + tensor var_727_end_0 = const()[name = tensor("op_727_end_0"), val = tensor([16, 589])]; + tensor var_727_end_mask_0 = const()[name = tensor("op_727_end_mask_0"), val = tensor([false, true])]; + tensor var_727_squeeze_mask_0 = const()[name = tensor("op_727_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_727_cast_fp16 = slice_by_index(begin = var_727_begin_0, end = var_727_end_0, end_mask = var_727_end_mask_0, squeeze_mask = var_727_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_727_cast_fp16")]; + tensor var_729_cast_fp16 = add(x = var_724_cast_fp16, y = var_727_cast_fp16)[name = tensor("op_729_cast_fp16")]; + tensor slice_by_index_30 = const()[name = tensor("slice_by_index_30"), val = tensor([883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471])]; + tensor scatter_30_mode_0 = const()[name = tensor("scatter_30_mode_0"), val = tensor("update")]; + tensor scatter_30_axis_0 = const()[name = tensor("scatter_30_axis_0"), val = tensor(0)]; + tensor scatter_30_cast_fp16 = scatter(axis = scatter_30_axis_0, data = scatter_28_cast_fp16, indices = slice_by_index_30, mode = scatter_30_mode_0, updates = var_729_cast_fp16)[name = tensor("scatter_30_cast_fp16")]; + tensor var_739_begin_0 = const()[name = tensor("op_739_begin_0"), val = tensor([883])]; + tensor var_739_end_0 = const()[name = tensor("op_739_end_0"), val = tensor([1472])]; + tensor var_739_end_mask_0 = const()[name = tensor("op_739_end_mask_0"), val = tensor([false])]; + tensor var_739_cast_fp16 = slice_by_index(begin = var_739_begin_0, end = var_739_end_0, end_mask = var_739_end_mask_0, x = scatter_29_cast_fp16)[name = tensor("op_739_cast_fp16")]; + tensor var_740_to_fp16 = const()[name = tensor("op_740_to_fp16"), val = tensor(0x1p+0)]; + tensor var_742_cast_fp16 = add(x = var_739_cast_fp16, y = var_740_to_fp16)[name = tensor("op_742_cast_fp16")]; + tensor scatter_31_mode_0 = const()[name = tensor("scatter_31_mode_0"), val = tensor("update")]; + tensor scatter_31_axis_0 = const()[name = tensor("scatter_31_axis_0"), val = tensor(0)]; + tensor scatter_31_cast_fp16 = scatter(axis = scatter_31_axis_0, data = scatter_29_cast_fp16, indices = slice_by_index_30, mode = scatter_31_mode_0, updates = var_742_cast_fp16)[name = tensor("scatter_31_cast_fp16")]; + tensor var_759_begin_0 = const()[name = tensor("op_759_begin_0"), val = tensor([942])]; + tensor var_759_end_0 = const()[name = tensor("op_759_end_0"), val = tensor([1531])]; + tensor var_759_end_mask_0 = const()[name = tensor("op_759_end_mask_0"), val = tensor([false])]; + tensor var_759_cast_fp16 = slice_by_index(begin = var_759_begin_0, end = var_759_end_0, end_mask = var_759_end_mask_0, x = scatter_30_cast_fp16)[name = tensor("op_759_cast_fp16")]; + tensor var_762_begin_0 = const()[name = tensor("op_762_begin_0"), val = tensor([16, 0])]; + tensor var_762_end_0 = const()[name = tensor("op_762_end_0"), val = tensor([17, 589])]; + tensor var_762_end_mask_0 = const()[name = tensor("op_762_end_mask_0"), val = tensor([false, true])]; + tensor var_762_squeeze_mask_0 = const()[name = tensor("op_762_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_762_cast_fp16 = slice_by_index(begin = var_762_begin_0, end = var_762_end_0, end_mask = var_762_end_mask_0, squeeze_mask = var_762_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_762_cast_fp16")]; + tensor var_764_cast_fp16 = add(x = var_759_cast_fp16, y = var_762_cast_fp16)[name = tensor("op_764_cast_fp16")]; + tensor slice_by_index_32 = const()[name = tensor("slice_by_index_32"), val = tensor([942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523, 1524, 1525, 1526, 1527, 1528, 1529, 1530])]; + tensor scatter_32_mode_0 = const()[name = tensor("scatter_32_mode_0"), val = tensor("update")]; + tensor scatter_32_axis_0 = const()[name = tensor("scatter_32_axis_0"), val = tensor(0)]; + tensor scatter_32_cast_fp16 = scatter(axis = scatter_32_axis_0, data = scatter_30_cast_fp16, indices = slice_by_index_32, mode = scatter_32_mode_0, updates = var_764_cast_fp16)[name = tensor("scatter_32_cast_fp16")]; + tensor var_774_begin_0 = const()[name = tensor("op_774_begin_0"), val = tensor([942])]; + tensor var_774_end_0 = const()[name = tensor("op_774_end_0"), val = tensor([1531])]; + tensor var_774_end_mask_0 = const()[name = tensor("op_774_end_mask_0"), val = tensor([false])]; + tensor var_774_cast_fp16 = slice_by_index(begin = var_774_begin_0, end = var_774_end_0, end_mask = var_774_end_mask_0, x = scatter_31_cast_fp16)[name = tensor("op_774_cast_fp16")]; + tensor var_775_to_fp16 = const()[name = tensor("op_775_to_fp16"), val = tensor(0x1p+0)]; + tensor var_777_cast_fp16 = add(x = var_774_cast_fp16, y = var_775_to_fp16)[name = tensor("op_777_cast_fp16")]; + tensor scatter_33_mode_0 = const()[name = tensor("scatter_33_mode_0"), val = tensor("update")]; + tensor scatter_33_axis_0 = const()[name = tensor("scatter_33_axis_0"), val = tensor(0)]; + tensor scatter_33_cast_fp16 = scatter(axis = scatter_33_axis_0, data = scatter_31_cast_fp16, indices = slice_by_index_32, mode = scatter_33_mode_0, updates = var_777_cast_fp16)[name = tensor("scatter_33_cast_fp16")]; + tensor var_794_begin_0 = const()[name = tensor("op_794_begin_0"), val = tensor([1001])]; + tensor var_794_end_0 = const()[name = tensor("op_794_end_0"), val = tensor([1590])]; + tensor var_794_end_mask_0 = const()[name = tensor("op_794_end_mask_0"), val = tensor([false])]; + tensor var_794_cast_fp16 = slice_by_index(begin = var_794_begin_0, end = var_794_end_0, end_mask = var_794_end_mask_0, x = scatter_32_cast_fp16)[name = tensor("op_794_cast_fp16")]; + tensor var_797_begin_0 = const()[name = tensor("op_797_begin_0"), val = tensor([17, 0])]; + tensor var_797_end_0 = const()[name = tensor("op_797_end_0"), val = tensor([18, 589])]; + tensor var_797_end_mask_0 = const()[name = tensor("op_797_end_mask_0"), val = tensor([false, true])]; + tensor var_797_squeeze_mask_0 = const()[name = tensor("op_797_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_797_cast_fp16 = slice_by_index(begin = var_797_begin_0, end = var_797_end_0, end_mask = var_797_end_mask_0, squeeze_mask = var_797_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_797_cast_fp16")]; + tensor var_799_cast_fp16 = add(x = var_794_cast_fp16, y = var_797_cast_fp16)[name = tensor("op_799_cast_fp16")]; + tensor slice_by_index_34 = const()[name = tensor("slice_by_index_34"), val = tensor([1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523, 1524, 1525, 1526, 1527, 1528, 1529, 1530, 1531, 1532, 1533, 1534, 1535, 1536, 1537, 1538, 1539, 1540, 1541, 1542, 1543, 1544, 1545, 1546, 1547, 1548, 1549, 1550, 1551, 1552, 1553, 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1568, 1569, 1570, 1571, 1572, 1573, 1574, 1575, 1576, 1577, 1578, 1579, 1580, 1581, 1582, 1583, 1584, 1585, 1586, 1587, 1588, 1589])]; + tensor scatter_34_mode_0 = const()[name = tensor("scatter_34_mode_0"), val = tensor("update")]; + tensor scatter_34_axis_0 = const()[name = tensor("scatter_34_axis_0"), val = tensor(0)]; + tensor scatter_34_cast_fp16 = scatter(axis = scatter_34_axis_0, data = scatter_32_cast_fp16, indices = slice_by_index_34, mode = scatter_34_mode_0, updates = var_799_cast_fp16)[name = tensor("scatter_34_cast_fp16")]; + tensor var_809_begin_0 = const()[name = tensor("op_809_begin_0"), val = tensor([1001])]; + tensor var_809_end_0 = const()[name = tensor("op_809_end_0"), val = tensor([1590])]; + tensor var_809_end_mask_0 = const()[name = tensor("op_809_end_mask_0"), val = tensor([false])]; + tensor var_809_cast_fp16 = slice_by_index(begin = var_809_begin_0, end = var_809_end_0, end_mask = var_809_end_mask_0, x = scatter_33_cast_fp16)[name = tensor("op_809_cast_fp16")]; + tensor var_810_to_fp16 = const()[name = tensor("op_810_to_fp16"), val = tensor(0x1p+0)]; + tensor var_812_cast_fp16 = add(x = var_809_cast_fp16, y = var_810_to_fp16)[name = tensor("op_812_cast_fp16")]; + tensor scatter_35_mode_0 = const()[name = tensor("scatter_35_mode_0"), val = tensor("update")]; + tensor scatter_35_axis_0 = const()[name = tensor("scatter_35_axis_0"), val = tensor(0)]; + tensor scatter_35_cast_fp16 = scatter(axis = scatter_35_axis_0, data = scatter_33_cast_fp16, indices = slice_by_index_34, mode = scatter_35_mode_0, updates = var_812_cast_fp16)[name = tensor("scatter_35_cast_fp16")]; + tensor var_829_begin_0 = const()[name = tensor("op_829_begin_0"), val = tensor([1060])]; + tensor var_829_end_0 = const()[name = tensor("op_829_end_0"), val = tensor([1649])]; + tensor var_829_end_mask_0 = const()[name = tensor("op_829_end_mask_0"), val = tensor([false])]; + tensor var_829_cast_fp16 = slice_by_index(begin = var_829_begin_0, end = var_829_end_0, end_mask = var_829_end_mask_0, x = scatter_34_cast_fp16)[name = tensor("op_829_cast_fp16")]; + tensor var_832_begin_0 = const()[name = tensor("op_832_begin_0"), val = tensor([18, 0])]; + tensor var_832_end_0 = const()[name = tensor("op_832_end_0"), val = tensor([19, 589])]; + tensor var_832_end_mask_0 = const()[name = tensor("op_832_end_mask_0"), val = tensor([false, true])]; + tensor var_832_squeeze_mask_0 = const()[name = tensor("op_832_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_832_cast_fp16 = slice_by_index(begin = var_832_begin_0, end = var_832_end_0, end_mask = var_832_end_mask_0, squeeze_mask = var_832_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_832_cast_fp16")]; + tensor var_834_cast_fp16 = add(x = var_829_cast_fp16, y = var_832_cast_fp16)[name = tensor("op_834_cast_fp16")]; + tensor slice_by_index_36 = const()[name = tensor("slice_by_index_36"), val = tensor([1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523, 1524, 1525, 1526, 1527, 1528, 1529, 1530, 1531, 1532, 1533, 1534, 1535, 1536, 1537, 1538, 1539, 1540, 1541, 1542, 1543, 1544, 1545, 1546, 1547, 1548, 1549, 1550, 1551, 1552, 1553, 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1568, 1569, 1570, 1571, 1572, 1573, 1574, 1575, 1576, 1577, 1578, 1579, 1580, 1581, 1582, 1583, 1584, 1585, 1586, 1587, 1588, 1589, 1590, 1591, 1592, 1593, 1594, 1595, 1596, 1597, 1598, 1599, 1600, 1601, 1602, 1603, 1604, 1605, 1606, 1607, 1608, 1609, 1610, 1611, 1612, 1613, 1614, 1615, 1616, 1617, 1618, 1619, 1620, 1621, 1622, 1623, 1624, 1625, 1626, 1627, 1628, 1629, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1638, 1639, 1640, 1641, 1642, 1643, 1644, 1645, 1646, 1647, 1648])]; + tensor scatter_36_mode_0 = const()[name = tensor("scatter_36_mode_0"), val = tensor("update")]; + tensor scatter_36_axis_0 = const()[name = tensor("scatter_36_axis_0"), val = tensor(0)]; + tensor scatter_36_cast_fp16 = scatter(axis = scatter_36_axis_0, data = scatter_34_cast_fp16, indices = slice_by_index_36, mode = scatter_36_mode_0, updates = var_834_cast_fp16)[name = tensor("scatter_36_cast_fp16")]; + tensor var_844_begin_0 = const()[name = tensor("op_844_begin_0"), val = tensor([1060])]; + tensor var_844_end_0 = const()[name = tensor("op_844_end_0"), val = tensor([1649])]; + tensor var_844_end_mask_0 = const()[name = tensor("op_844_end_mask_0"), val = tensor([false])]; + tensor var_844_cast_fp16 = slice_by_index(begin = var_844_begin_0, end = var_844_end_0, end_mask = var_844_end_mask_0, x = scatter_35_cast_fp16)[name = tensor("op_844_cast_fp16")]; + tensor var_845_to_fp16 = const()[name = tensor("op_845_to_fp16"), val = tensor(0x1p+0)]; + tensor var_847_cast_fp16 = add(x = var_844_cast_fp16, y = var_845_to_fp16)[name = tensor("op_847_cast_fp16")]; + tensor scatter_37_mode_0 = const()[name = tensor("scatter_37_mode_0"), val = tensor("update")]; + tensor scatter_37_axis_0 = const()[name = tensor("scatter_37_axis_0"), val = tensor(0)]; + tensor scatter_37_cast_fp16 = scatter(axis = scatter_37_axis_0, data = scatter_35_cast_fp16, indices = slice_by_index_36, mode = scatter_37_mode_0, updates = var_847_cast_fp16)[name = tensor("scatter_37_cast_fp16")]; + tensor var_864_begin_0 = const()[name = tensor("op_864_begin_0"), val = tensor([1119])]; + tensor var_864_end_0 = const()[name = tensor("op_864_end_0"), val = tensor([1708])]; + tensor var_864_end_mask_0 = const()[name = tensor("op_864_end_mask_0"), val = tensor([false])]; + tensor var_864_cast_fp16 = slice_by_index(begin = var_864_begin_0, end = var_864_end_0, end_mask = var_864_end_mask_0, x = scatter_36_cast_fp16)[name = tensor("op_864_cast_fp16")]; + tensor var_867_begin_0 = const()[name = tensor("op_867_begin_0"), val = tensor([19, 0])]; + tensor var_867_end_0 = const()[name = tensor("op_867_end_0"), val = tensor([20, 589])]; + tensor var_867_end_mask_0 = const()[name = tensor("op_867_end_mask_0"), val = tensor([false, true])]; + tensor var_867_squeeze_mask_0 = const()[name = tensor("op_867_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_867_cast_fp16 = slice_by_index(begin = var_867_begin_0, end = var_867_end_0, end_mask = var_867_end_mask_0, squeeze_mask = var_867_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_867_cast_fp16")]; + tensor var_869_cast_fp16 = add(x = var_864_cast_fp16, y = var_867_cast_fp16)[name = tensor("op_869_cast_fp16")]; + tensor slice_by_index_38 = const()[name = tensor("slice_by_index_38"), val = tensor([1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523, 1524, 1525, 1526, 1527, 1528, 1529, 1530, 1531, 1532, 1533, 1534, 1535, 1536, 1537, 1538, 1539, 1540, 1541, 1542, 1543, 1544, 1545, 1546, 1547, 1548, 1549, 1550, 1551, 1552, 1553, 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1568, 1569, 1570, 1571, 1572, 1573, 1574, 1575, 1576, 1577, 1578, 1579, 1580, 1581, 1582, 1583, 1584, 1585, 1586, 1587, 1588, 1589, 1590, 1591, 1592, 1593, 1594, 1595, 1596, 1597, 1598, 1599, 1600, 1601, 1602, 1603, 1604, 1605, 1606, 1607, 1608, 1609, 1610, 1611, 1612, 1613, 1614, 1615, 1616, 1617, 1618, 1619, 1620, 1621, 1622, 1623, 1624, 1625, 1626, 1627, 1628, 1629, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1638, 1639, 1640, 1641, 1642, 1643, 1644, 1645, 1646, 1647, 1648, 1649, 1650, 1651, 1652, 1653, 1654, 1655, 1656, 1657, 1658, 1659, 1660, 1661, 1662, 1663, 1664, 1665, 1666, 1667, 1668, 1669, 1670, 1671, 1672, 1673, 1674, 1675, 1676, 1677, 1678, 1679, 1680, 1681, 1682, 1683, 1684, 1685, 1686, 1687, 1688, 1689, 1690, 1691, 1692, 1693, 1694, 1695, 1696, 1697, 1698, 1699, 1700, 1701, 1702, 1703, 1704, 1705, 1706, 1707])]; + tensor scatter_38_mode_0 = const()[name = tensor("scatter_38_mode_0"), val = tensor("update")]; + tensor scatter_38_axis_0 = const()[name = tensor("scatter_38_axis_0"), val = tensor(0)]; + tensor scatter_38_cast_fp16 = scatter(axis = scatter_38_axis_0, data = scatter_36_cast_fp16, indices = slice_by_index_38, mode = scatter_38_mode_0, updates = var_869_cast_fp16)[name = tensor("scatter_38_cast_fp16")]; + tensor var_879_begin_0 = const()[name = tensor("op_879_begin_0"), val = tensor([1119])]; + tensor var_879_end_0 = const()[name = tensor("op_879_end_0"), val = tensor([1708])]; + tensor var_879_end_mask_0 = const()[name = tensor("op_879_end_mask_0"), val = tensor([false])]; + tensor var_879_cast_fp16 = slice_by_index(begin = var_879_begin_0, end = var_879_end_0, end_mask = var_879_end_mask_0, x = scatter_37_cast_fp16)[name = tensor("op_879_cast_fp16")]; + tensor var_880_to_fp16 = const()[name = tensor("op_880_to_fp16"), val = tensor(0x1p+0)]; + tensor var_882_cast_fp16 = add(x = var_879_cast_fp16, y = var_880_to_fp16)[name = tensor("op_882_cast_fp16")]; + tensor scatter_39_mode_0 = const()[name = tensor("scatter_39_mode_0"), val = tensor("update")]; + tensor scatter_39_axis_0 = const()[name = tensor("scatter_39_axis_0"), val = tensor(0)]; + tensor scatter_39_cast_fp16 = scatter(axis = scatter_39_axis_0, data = scatter_37_cast_fp16, indices = slice_by_index_38, mode = scatter_39_mode_0, updates = var_882_cast_fp16)[name = tensor("scatter_39_cast_fp16")]; + tensor var_899_begin_0 = const()[name = tensor("op_899_begin_0"), val = tensor([1178])]; + tensor var_899_end_0 = const()[name = tensor("op_899_end_0"), val = tensor([1767])]; + tensor var_899_end_mask_0 = const()[name = tensor("op_899_end_mask_0"), val = tensor([false])]; + tensor var_899_cast_fp16 = slice_by_index(begin = var_899_begin_0, end = var_899_end_0, end_mask = var_899_end_mask_0, x = scatter_38_cast_fp16)[name = tensor("op_899_cast_fp16")]; + tensor var_902_begin_0 = const()[name = tensor("op_902_begin_0"), val = tensor([20, 0])]; + tensor var_902_end_0 = const()[name = tensor("op_902_end_0"), val = tensor([21, 589])]; + tensor var_902_end_mask_0 = const()[name = tensor("op_902_end_mask_0"), val = tensor([false, true])]; + tensor var_902_squeeze_mask_0 = const()[name = tensor("op_902_squeeze_mask_0"), val = tensor([true, false])]; + tensor var_902_cast_fp16 = slice_by_index(begin = var_902_begin_0, end = var_902_end_0, end_mask = var_902_end_mask_0, squeeze_mask = var_902_squeeze_mask_0, x = reduce_max_0_cast_fp16)[name = tensor("op_902_cast_fp16")]; + tensor var_904_cast_fp16 = add(x = var_899_cast_fp16, y = var_902_cast_fp16)[name = tensor("op_904_cast_fp16")]; + tensor slice_by_index_40 = const()[name = tensor("slice_by_index_40"), val = tensor([1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523, 1524, 1525, 1526, 1527, 1528, 1529, 1530, 1531, 1532, 1533, 1534, 1535, 1536, 1537, 1538, 1539, 1540, 1541, 1542, 1543, 1544, 1545, 1546, 1547, 1548, 1549, 1550, 1551, 1552, 1553, 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1568, 1569, 1570, 1571, 1572, 1573, 1574, 1575, 1576, 1577, 1578, 1579, 1580, 1581, 1582, 1583, 1584, 1585, 1586, 1587, 1588, 1589, 1590, 1591, 1592, 1593, 1594, 1595, 1596, 1597, 1598, 1599, 1600, 1601, 1602, 1603, 1604, 1605, 1606, 1607, 1608, 1609, 1610, 1611, 1612, 1613, 1614, 1615, 1616, 1617, 1618, 1619, 1620, 1621, 1622, 1623, 1624, 1625, 1626, 1627, 1628, 1629, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1638, 1639, 1640, 1641, 1642, 1643, 1644, 1645, 1646, 1647, 1648, 1649, 1650, 1651, 1652, 1653, 1654, 1655, 1656, 1657, 1658, 1659, 1660, 1661, 1662, 1663, 1664, 1665, 1666, 1667, 1668, 1669, 1670, 1671, 1672, 1673, 1674, 1675, 1676, 1677, 1678, 1679, 1680, 1681, 1682, 1683, 1684, 1685, 1686, 1687, 1688, 1689, 1690, 1691, 1692, 1693, 1694, 1695, 1696, 1697, 1698, 1699, 1700, 1701, 1702, 1703, 1704, 1705, 1706, 1707, 1708, 1709, 1710, 1711, 1712, 1713, 1714, 1715, 1716, 1717, 1718, 1719, 1720, 1721, 1722, 1723, 1724, 1725, 1726, 1727, 1728, 1729, 1730, 1731, 1732, 1733, 1734, 1735, 1736, 1737, 1738, 1739, 1740, 1741, 1742, 1743, 1744, 1745, 1746, 1747, 1748, 1749, 1750, 1751, 1752, 1753, 1754, 1755, 1756, 1757, 1758, 1759, 1760, 1761, 1762, 1763, 1764, 1765, 1766])]; + tensor scatter_40_mode_0 = const()[name = tensor("scatter_40_mode_0"), val = tensor("update")]; + tensor scatter_40_axis_0 = const()[name = tensor("scatter_40_axis_0"), val = tensor(0)]; + tensor scatter_40_cast_fp16 = scatter(axis = scatter_40_axis_0, data = scatter_38_cast_fp16, indices = slice_by_index_40, mode = scatter_40_mode_0, updates = var_904_cast_fp16)[name = tensor("scatter_40_cast_fp16")]; + tensor var_914_begin_0 = const()[name = tensor("op_914_begin_0"), val = tensor([1178])]; + tensor var_914_end_0 = const()[name = tensor("op_914_end_0"), val = tensor([1767])]; + tensor var_914_end_mask_0 = const()[name = tensor("op_914_end_mask_0"), val = tensor([false])]; + tensor var_914_cast_fp16 = slice_by_index(begin = var_914_begin_0, end = var_914_end_0, end_mask = var_914_end_mask_0, x = scatter_39_cast_fp16)[name = tensor("op_914_cast_fp16")]; + tensor var_915_to_fp16 = const()[name = tensor("op_915_to_fp16"), val = tensor(0x1p+0)]; + tensor var_917_cast_fp16 = add(x = var_914_cast_fp16, y = var_915_to_fp16)[name = tensor("op_917_cast_fp16")]; + tensor scatter_41_mode_0 = const()[name = tensor("scatter_41_mode_0"), val = tensor("update")]; + tensor scatter_41_axis_0 = const()[name = tensor("scatter_41_axis_0"), val = tensor(0)]; + tensor scatter_41_cast_fp16 = scatter(axis = scatter_41_axis_0, data = scatter_39_cast_fp16, indices = slice_by_index_40, mode = scatter_41_mode_0, updates = var_917_cast_fp16)[name = tensor("scatter_41_cast_fp16")]; + tensor voice_activity = real_div(x = scatter_40_cast_fp16, y = scatter_41_cast_fp16)[name = tensor("op_924_cast_fp16")]; + tensor var_929_axes_0 = const()[name = tensor("op_929_axes_0"), val = tensor([1])]; + tensor var_929_keep_dims_0 = const()[name = tensor("op_929_keep_dims_0"), val = tensor(false)]; + tensor speaker_activity = reduce_sum(axes = var_929_axes_0, keep_dims = var_929_keep_dims_0, x = speaker_ids)[name = tensor("op_929_cast_fp16")]; + tensor var_934_axes_0 = const()[name = tensor("op_934_axes_0"), val = tensor([2])]; + tensor var_934_keep_dims_0 = const()[name = tensor("op_934_keep_dims_0"), val = tensor(false)]; + tensor var_934_cast_fp16 = reduce_sum(axes = var_934_axes_0, keep_dims = var_934_keep_dims_0, x = speaker_ids)[name = tensor("op_934_cast_fp16")]; + tensor var_935_to_fp16 = const()[name = tensor("op_935_to_fp16"), val = tensor(0x1p+0)]; + tensor var_936_cast_fp16 = greater(x = var_934_cast_fp16, y = var_935_to_fp16)[name = tensor("op_936_cast_fp16")]; + tensor cast_8_dtype_0 = const()[name = tensor("cast_8_dtype_0"), val = tensor("fp16")]; + tensor overlapped_speaker_activity = cast(dtype = cast_8_dtype_0, x = var_936_cast_fp16)[name = tensor("cast_0")]; + } -> (speaker_probs, speaker_ids, speaker_activity, overlapped_speaker_activity, voice_activity, sliding_window_waveform); +} \ No newline at end of file diff --git a/speaker_segmenter/pyannote-v3/W8A32/SpeakerSegmenter.mlmodelc/weights/weight.bin 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