diff --git "a/vocoder/Vocoder282.mlmodelc/model.mil" "b/vocoder/Vocoder282.mlmodelc/model.mil" new file mode 100644--- /dev/null +++ "b/vocoder/Vocoder282.mlmodelc/model.mil" @@ -0,0 +1,607 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.12.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] +{ + func main(tensor mel) { + tensor x_1_pad_type_0 = const()[name = tensor("x_1_pad_type_0"), val = tensor("custom")]; + tensor x_1_pad_0 = const()[name = tensor("x_1_pad_0"), val = tensor([3, 3])]; + tensor x_1_strides_0 = const()[name = tensor("x_1_strides_0"), val = tensor([1])]; + tensor x_1_dilations_0 = const()[name = tensor("x_1_dilations_0"), val = tensor([1])]; + tensor x_1_groups_0 = const()[name = tensor("x_1_groups_0"), val = tensor(1)]; + tensor mel_to_fp16_dtype_0 = const()[name = tensor("mel_to_fp16_dtype_0"), val = tensor("fp16")]; + tensor backbone_embed_weight_to_fp16 = const()[name = tensor("backbone_embed_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor backbone_embed_bias_to_fp16 = const()[name = tensor("backbone_embed_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(716928)))]; + tensor mel_to_fp16 = cast(dtype = mel_to_fp16_dtype_0, x = mel)[name = tensor("cast_10")]; + tensor x_1_cast_fp16 = conv(bias = backbone_embed_bias_to_fp16, dilations = x_1_dilations_0, groups = x_1_groups_0, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = x_1_strides_0, weight = backbone_embed_weight_to_fp16, x = mel_to_fp16)[name = tensor("x_1_cast_fp16")]; + tensor input_1_perm_0 = const()[name = tensor("input_1_perm_0"), val = tensor([0, 2, 1])]; + tensor x_3_axes_0 = const()[name = tensor("x_3_axes_0"), val = tensor([-1])]; + tensor backbone_norm_weight_to_fp16 = const()[name = tensor("backbone_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(718016)))]; + tensor backbone_norm_bias_to_fp16 = const()[name = tensor("backbone_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(719104)))]; + tensor var_10_to_fp16 = const()[name = tensor("op_10_to_fp16"), val = tensor(0x1.1p-20)]; + tensor input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = x_1_cast_fp16)[name = tensor("transpose_31")]; + tensor x_3_cast_fp16 = layer_norm(axes = x_3_axes_0, beta = backbone_norm_bias_to_fp16, epsilon = var_10_to_fp16, gamma = backbone_norm_weight_to_fp16, x = input_1_cast_fp16)[name = tensor("x_3_cast_fp16")]; + tensor x_5_pad_type_0 = const()[name = tensor("x_5_pad_type_0"), val = tensor("custom")]; + tensor x_5_pad_0 = const()[name = tensor("x_5_pad_0"), val = tensor([3, 3])]; + tensor x_5_groups_0 = const()[name = tensor("x_5_groups_0"), val = tensor(512)]; + tensor x_5_strides_0 = const()[name = tensor("x_5_strides_0"), val = tensor([1])]; + tensor x_5_dilations_0 = const()[name = tensor("x_5_dilations_0"), val = tensor([1])]; + tensor transpose_4_perm_0 = const()[name = tensor("transpose_4_perm_0"), val = tensor([0, 2, 1])]; + tensor backbone_convnext_0_dwconv_weight_to_fp16 = const()[name = tensor("backbone_convnext_0_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(720192)))]; + tensor backbone_convnext_0_dwconv_bias_to_fp16 = const()[name = tensor("backbone_convnext_0_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(727424)))]; + tensor transpose_4_cast_fp16 = transpose(perm = transpose_4_perm_0, x = x_3_cast_fp16)[name = tensor("transpose_30")]; + tensor x_5_cast_fp16 = conv(bias = backbone_convnext_0_dwconv_bias_to_fp16, dilations = x_5_dilations_0, groups = x_5_groups_0, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = x_5_strides_0, weight = backbone_convnext_0_dwconv_weight_to_fp16, x = transpose_4_cast_fp16)[name = tensor("x_5_cast_fp16")]; + tensor input_5_perm_0 = const()[name = tensor("input_5_perm_0"), val = tensor([0, 2, 1])]; + tensor input_7_axes_0 = const()[name = tensor("input_7_axes_0"), val = tensor([-1])]; + tensor backbone_convnext_0_norm_weight_to_fp16 = const()[name = tensor("backbone_convnext_0_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(728512)))]; + tensor backbone_convnext_0_norm_bias_to_fp16 = const()[name = tensor("backbone_convnext_0_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(729600)))]; + tensor input_5_cast_fp16 = transpose(perm = input_5_perm_0, x = x_5_cast_fp16)[name = tensor("transpose_29")]; + tensor input_7_cast_fp16 = layer_norm(axes = input_7_axes_0, beta = backbone_convnext_0_norm_bias_to_fp16, epsilon = var_10_to_fp16, gamma = backbone_convnext_0_norm_weight_to_fp16, x = input_5_cast_fp16)[name = tensor("input_7_cast_fp16")]; + tensor backbone_convnext_0_pwconv1_weight_to_fp16 = const()[name = tensor("backbone_convnext_0_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(730688)))]; + tensor backbone_convnext_0_pwconv1_bias_to_fp16 = const()[name = tensor("backbone_convnext_0_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2303616)))]; + tensor linear_0_cast_fp16 = linear(bias = backbone_convnext_0_pwconv1_bias_to_fp16, weight = backbone_convnext_0_pwconv1_weight_to_fp16, x = input_7_cast_fp16)[name = tensor("linear_0_cast_fp16")]; + tensor input_11_mode_0 = const()[name = tensor("input_11_mode_0"), val = tensor("EXACT")]; + tensor input_11_cast_fp16 = gelu(mode = input_11_mode_0, x = linear_0_cast_fp16)[name = tensor("input_11_cast_fp16")]; + tensor backbone_convnext_0_pwconv2_weight_to_fp16 = const()[name = tensor("backbone_convnext_0_pwconv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2306752)))]; + tensor backbone_convnext_0_pwconv2_bias_to_fp16 = const()[name = tensor("backbone_convnext_0_pwconv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3879680)))]; + tensor linear_1_cast_fp16 = linear(bias = backbone_convnext_0_pwconv2_bias_to_fp16, weight = backbone_convnext_0_pwconv2_weight_to_fp16, x = input_11_cast_fp16)[name = tensor("linear_1_cast_fp16")]; + tensor backbone_convnext_0_gamma_to_fp16 = const()[name = tensor("backbone_convnext_0_gamma_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3880768)))]; + tensor x_9_cast_fp16 = mul(x = backbone_convnext_0_gamma_to_fp16, y = linear_1_cast_fp16)[name = tensor("x_9_cast_fp16")]; + tensor input_13_cast_fp16 = add(x = x_3_cast_fp16, y = x_9_cast_fp16)[name = tensor("input_13_cast_fp16")]; + tensor x_13_pad_type_0 = const()[name = tensor("x_13_pad_type_0"), val = tensor("custom")]; + tensor x_13_pad_0 = const()[name = tensor("x_13_pad_0"), val = tensor([3, 3])]; + tensor x_13_groups_0 = const()[name = tensor("x_13_groups_0"), val = tensor(512)]; + tensor x_13_strides_0 = const()[name = tensor("x_13_strides_0"), val = tensor([1])]; + tensor x_13_dilations_0 = const()[name = tensor("x_13_dilations_0"), val = tensor([1])]; + tensor transpose_5_perm_0 = const()[name = tensor("transpose_5_perm_0"), val = tensor([0, 2, 1])]; + tensor backbone_convnext_1_dwconv_weight_to_fp16 = const()[name = tensor("backbone_convnext_1_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3881856)))]; + tensor backbone_convnext_1_dwconv_bias_to_fp16 = const()[name = tensor("backbone_convnext_1_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3889088)))]; + tensor transpose_5_cast_fp16 = transpose(perm = transpose_5_perm_0, x = input_13_cast_fp16)[name = tensor("transpose_28")]; + tensor x_13_cast_fp16 = conv(bias = backbone_convnext_1_dwconv_bias_to_fp16, dilations = x_13_dilations_0, groups = x_13_groups_0, pad = x_13_pad_0, pad_type = x_13_pad_type_0, strides = x_13_strides_0, weight = backbone_convnext_1_dwconv_weight_to_fp16, x = transpose_5_cast_fp16)[name = tensor("x_13_cast_fp16")]; + tensor input_15_perm_0 = const()[name = tensor("input_15_perm_0"), val = tensor([0, 2, 1])]; + tensor input_17_axes_0 = const()[name = tensor("input_17_axes_0"), val = tensor([-1])]; + tensor backbone_convnext_1_norm_weight_to_fp16 = const()[name = tensor("backbone_convnext_1_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3890176)))]; + tensor backbone_convnext_1_norm_bias_to_fp16 = const()[name = tensor("backbone_convnext_1_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3891264)))]; + tensor input_15_cast_fp16 = transpose(perm = input_15_perm_0, x = x_13_cast_fp16)[name = tensor("transpose_27")]; + tensor input_17_cast_fp16 = layer_norm(axes = input_17_axes_0, beta = backbone_convnext_1_norm_bias_to_fp16, epsilon = var_10_to_fp16, gamma = backbone_convnext_1_norm_weight_to_fp16, x = input_15_cast_fp16)[name = tensor("input_17_cast_fp16")]; + tensor backbone_convnext_1_pwconv1_weight_to_fp16 = const()[name = tensor("backbone_convnext_1_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3892352)))]; + tensor backbone_convnext_1_pwconv1_bias_to_fp16 = const()[name = tensor("backbone_convnext_1_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5465280)))]; + tensor linear_2_cast_fp16 = linear(bias = backbone_convnext_1_pwconv1_bias_to_fp16, weight = backbone_convnext_1_pwconv1_weight_to_fp16, x = input_17_cast_fp16)[name = tensor("linear_2_cast_fp16")]; + tensor input_21_mode_0 = const()[name = tensor("input_21_mode_0"), val = tensor("EXACT")]; + tensor input_21_cast_fp16 = gelu(mode = input_21_mode_0, x = linear_2_cast_fp16)[name = tensor("input_21_cast_fp16")]; + tensor backbone_convnext_1_pwconv2_weight_to_fp16 = const()[name = tensor("backbone_convnext_1_pwconv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5468416)))]; + tensor backbone_convnext_1_pwconv2_bias_to_fp16 = const()[name = tensor("backbone_convnext_1_pwconv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7041344)))]; + tensor linear_3_cast_fp16 = linear(bias = backbone_convnext_1_pwconv2_bias_to_fp16, weight = backbone_convnext_1_pwconv2_weight_to_fp16, x = input_21_cast_fp16)[name = tensor("linear_3_cast_fp16")]; + tensor backbone_convnext_1_gamma_to_fp16 = const()[name = tensor("backbone_convnext_1_gamma_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7042432)))]; + tensor x_17_cast_fp16 = mul(x = backbone_convnext_1_gamma_to_fp16, y = linear_3_cast_fp16)[name = tensor("x_17_cast_fp16")]; + tensor input_23_cast_fp16 = add(x = input_13_cast_fp16, y = x_17_cast_fp16)[name = tensor("input_23_cast_fp16")]; + tensor x_21_pad_type_0 = const()[name = tensor("x_21_pad_type_0"), val = tensor("custom")]; + tensor x_21_pad_0 = const()[name = tensor("x_21_pad_0"), val = tensor([3, 3])]; + tensor x_21_groups_0 = const()[name = tensor("x_21_groups_0"), val = tensor(512)]; + tensor x_21_strides_0 = const()[name = tensor("x_21_strides_0"), val = tensor([1])]; + tensor x_21_dilations_0 = const()[name = tensor("x_21_dilations_0"), val = tensor([1])]; + tensor transpose_6_perm_0 = const()[name = tensor("transpose_6_perm_0"), val = tensor([0, 2, 1])]; + tensor backbone_convnext_2_dwconv_weight_to_fp16 = const()[name = tensor("backbone_convnext_2_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7043520)))]; + tensor backbone_convnext_2_dwconv_bias_to_fp16 = const()[name = tensor("backbone_convnext_2_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7050752)))]; + tensor transpose_6_cast_fp16 = transpose(perm = transpose_6_perm_0, x = input_23_cast_fp16)[name = tensor("transpose_26")]; + tensor x_21_cast_fp16 = conv(bias = backbone_convnext_2_dwconv_bias_to_fp16, dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = backbone_convnext_2_dwconv_weight_to_fp16, x = transpose_6_cast_fp16)[name = tensor("x_21_cast_fp16")]; + tensor input_25_perm_0 = const()[name = tensor("input_25_perm_0"), val = tensor([0, 2, 1])]; + tensor input_27_axes_0 = const()[name = tensor("input_27_axes_0"), val = tensor([-1])]; + tensor backbone_convnext_2_norm_weight_to_fp16 = const()[name = tensor("backbone_convnext_2_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7051840)))]; + tensor backbone_convnext_2_norm_bias_to_fp16 = const()[name = tensor("backbone_convnext_2_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7052928)))]; + tensor input_25_cast_fp16 = transpose(perm = input_25_perm_0, x = x_21_cast_fp16)[name = tensor("transpose_25")]; + tensor input_27_cast_fp16 = layer_norm(axes = input_27_axes_0, beta = backbone_convnext_2_norm_bias_to_fp16, epsilon = var_10_to_fp16, gamma = backbone_convnext_2_norm_weight_to_fp16, x = input_25_cast_fp16)[name = tensor("input_27_cast_fp16")]; + tensor backbone_convnext_2_pwconv1_weight_to_fp16 = const()[name = tensor("backbone_convnext_2_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7054016)))]; + tensor backbone_convnext_2_pwconv1_bias_to_fp16 = const()[name = tensor("backbone_convnext_2_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8626944)))]; + tensor linear_4_cast_fp16 = linear(bias = backbone_convnext_2_pwconv1_bias_to_fp16, weight = backbone_convnext_2_pwconv1_weight_to_fp16, x = input_27_cast_fp16)[name = tensor("linear_4_cast_fp16")]; + tensor input_31_mode_0 = const()[name = tensor("input_31_mode_0"), val = tensor("EXACT")]; + tensor input_31_cast_fp16 = gelu(mode = input_31_mode_0, x = linear_4_cast_fp16)[name = tensor("input_31_cast_fp16")]; + tensor backbone_convnext_2_pwconv2_weight_to_fp16 = const()[name = tensor("backbone_convnext_2_pwconv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8630080)))]; + tensor backbone_convnext_2_pwconv2_bias_to_fp16 = const()[name = tensor("backbone_convnext_2_pwconv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10203008)))]; + tensor linear_5_cast_fp16 = linear(bias = backbone_convnext_2_pwconv2_bias_to_fp16, weight = backbone_convnext_2_pwconv2_weight_to_fp16, x = input_31_cast_fp16)[name = tensor("linear_5_cast_fp16")]; + tensor backbone_convnext_2_gamma_to_fp16 = const()[name = tensor("backbone_convnext_2_gamma_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10204096)))]; + tensor x_25_cast_fp16 = mul(x = backbone_convnext_2_gamma_to_fp16, y = linear_5_cast_fp16)[name = tensor("x_25_cast_fp16")]; + tensor input_33_cast_fp16 = add(x = input_23_cast_fp16, y = x_25_cast_fp16)[name = tensor("input_33_cast_fp16")]; + tensor x_29_pad_type_0 = const()[name = tensor("x_29_pad_type_0"), val = tensor("custom")]; + tensor x_29_pad_0 = const()[name = tensor("x_29_pad_0"), val = tensor([3, 3])]; + tensor x_29_groups_0 = const()[name = tensor("x_29_groups_0"), val = tensor(512)]; + tensor x_29_strides_0 = const()[name = tensor("x_29_strides_0"), val = tensor([1])]; + tensor x_29_dilations_0 = const()[name = tensor("x_29_dilations_0"), val = tensor([1])]; + tensor transpose_7_perm_0 = const()[name = tensor("transpose_7_perm_0"), val = tensor([0, 2, 1])]; + tensor backbone_convnext_3_dwconv_weight_to_fp16 = const()[name = tensor("backbone_convnext_3_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10205184)))]; + tensor backbone_convnext_3_dwconv_bias_to_fp16 = const()[name = tensor("backbone_convnext_3_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10212416)))]; + tensor transpose_7_cast_fp16 = transpose(perm = transpose_7_perm_0, x = input_33_cast_fp16)[name = tensor("transpose_24")]; + tensor x_29_cast_fp16 = conv(bias = backbone_convnext_3_dwconv_bias_to_fp16, dilations = x_29_dilations_0, groups = x_29_groups_0, pad = x_29_pad_0, pad_type = x_29_pad_type_0, strides = x_29_strides_0, weight = backbone_convnext_3_dwconv_weight_to_fp16, x = transpose_7_cast_fp16)[name = tensor("x_29_cast_fp16")]; + tensor input_35_perm_0 = const()[name = tensor("input_35_perm_0"), val = tensor([0, 2, 1])]; + tensor input_37_axes_0 = const()[name = tensor("input_37_axes_0"), val = tensor([-1])]; + tensor backbone_convnext_3_norm_weight_to_fp16 = const()[name = tensor("backbone_convnext_3_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10213504)))]; + tensor backbone_convnext_3_norm_bias_to_fp16 = const()[name = tensor("backbone_convnext_3_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10214592)))]; + tensor input_35_cast_fp16 = transpose(perm = input_35_perm_0, x = x_29_cast_fp16)[name = tensor("transpose_23")]; + tensor input_37_cast_fp16 = layer_norm(axes = input_37_axes_0, beta = backbone_convnext_3_norm_bias_to_fp16, epsilon = var_10_to_fp16, gamma = backbone_convnext_3_norm_weight_to_fp16, x = input_35_cast_fp16)[name = tensor("input_37_cast_fp16")]; + tensor backbone_convnext_3_pwconv1_weight_to_fp16 = const()[name = tensor("backbone_convnext_3_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10215680)))]; + tensor backbone_convnext_3_pwconv1_bias_to_fp16 = const()[name = tensor("backbone_convnext_3_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11788608)))]; + tensor linear_6_cast_fp16 = linear(bias = backbone_convnext_3_pwconv1_bias_to_fp16, weight = backbone_convnext_3_pwconv1_weight_to_fp16, x = input_37_cast_fp16)[name = tensor("linear_6_cast_fp16")]; + tensor input_41_mode_0 = const()[name = tensor("input_41_mode_0"), val = tensor("EXACT")]; + tensor input_41_cast_fp16 = gelu(mode = input_41_mode_0, x = linear_6_cast_fp16)[name = tensor("input_41_cast_fp16")]; + tensor backbone_convnext_3_pwconv2_weight_to_fp16 = const()[name = tensor("backbone_convnext_3_pwconv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11791744)))]; + tensor backbone_convnext_3_pwconv2_bias_to_fp16 = const()[name = tensor("backbone_convnext_3_pwconv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13364672)))]; + tensor linear_7_cast_fp16 = linear(bias = backbone_convnext_3_pwconv2_bias_to_fp16, weight = backbone_convnext_3_pwconv2_weight_to_fp16, x = input_41_cast_fp16)[name = tensor("linear_7_cast_fp16")]; + tensor backbone_convnext_3_gamma_to_fp16 = const()[name = tensor("backbone_convnext_3_gamma_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13365760)))]; + tensor x_33_cast_fp16 = mul(x = backbone_convnext_3_gamma_to_fp16, y = linear_7_cast_fp16)[name = tensor("x_33_cast_fp16")]; + tensor input_43_cast_fp16 = add(x = input_33_cast_fp16, y = x_33_cast_fp16)[name = tensor("input_43_cast_fp16")]; + tensor x_37_pad_type_0 = const()[name = tensor("x_37_pad_type_0"), val = tensor("custom")]; + tensor x_37_pad_0 = const()[name = tensor("x_37_pad_0"), val = tensor([3, 3])]; + tensor x_37_groups_0 = const()[name = tensor("x_37_groups_0"), val = tensor(512)]; + tensor x_37_strides_0 = const()[name = tensor("x_37_strides_0"), val = tensor([1])]; + tensor x_37_dilations_0 = const()[name = tensor("x_37_dilations_0"), val = tensor([1])]; + tensor transpose_8_perm_0 = const()[name = tensor("transpose_8_perm_0"), val = tensor([0, 2, 1])]; + tensor backbone_convnext_4_dwconv_weight_to_fp16 = const()[name = tensor("backbone_convnext_4_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13366848)))]; + tensor backbone_convnext_4_dwconv_bias_to_fp16 = const()[name = tensor("backbone_convnext_4_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13374080)))]; + tensor transpose_8_cast_fp16 = transpose(perm = transpose_8_perm_0, x = input_43_cast_fp16)[name = tensor("transpose_22")]; + tensor x_37_cast_fp16 = conv(bias = backbone_convnext_4_dwconv_bias_to_fp16, dilations = x_37_dilations_0, groups = x_37_groups_0, pad = x_37_pad_0, pad_type = x_37_pad_type_0, strides = x_37_strides_0, weight = backbone_convnext_4_dwconv_weight_to_fp16, x = transpose_8_cast_fp16)[name = tensor("x_37_cast_fp16")]; + tensor input_45_perm_0 = const()[name = tensor("input_45_perm_0"), val = tensor([0, 2, 1])]; + tensor input_47_axes_0 = const()[name = tensor("input_47_axes_0"), val = tensor([-1])]; + tensor backbone_convnext_4_norm_weight_to_fp16 = const()[name = tensor("backbone_convnext_4_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13375168)))]; + tensor backbone_convnext_4_norm_bias_to_fp16 = const()[name = tensor("backbone_convnext_4_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13376256)))]; + tensor input_45_cast_fp16 = transpose(perm = input_45_perm_0, x = x_37_cast_fp16)[name = tensor("transpose_21")]; + tensor input_47_cast_fp16 = layer_norm(axes = input_47_axes_0, beta = backbone_convnext_4_norm_bias_to_fp16, epsilon = var_10_to_fp16, gamma = backbone_convnext_4_norm_weight_to_fp16, x = input_45_cast_fp16)[name = tensor("input_47_cast_fp16")]; + tensor backbone_convnext_4_pwconv1_weight_to_fp16 = const()[name = tensor("backbone_convnext_4_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13377344)))]; + tensor backbone_convnext_4_pwconv1_bias_to_fp16 = const()[name = tensor("backbone_convnext_4_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14950272)))]; + tensor linear_8_cast_fp16 = linear(bias = backbone_convnext_4_pwconv1_bias_to_fp16, weight = backbone_convnext_4_pwconv1_weight_to_fp16, x = input_47_cast_fp16)[name = tensor("linear_8_cast_fp16")]; + tensor input_51_mode_0 = const()[name = tensor("input_51_mode_0"), val = tensor("EXACT")]; + tensor input_51_cast_fp16 = gelu(mode = input_51_mode_0, x = linear_8_cast_fp16)[name = tensor("input_51_cast_fp16")]; + tensor backbone_convnext_4_pwconv2_weight_to_fp16 = const()[name = tensor("backbone_convnext_4_pwconv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14953408)))]; + tensor backbone_convnext_4_pwconv2_bias_to_fp16 = const()[name = tensor("backbone_convnext_4_pwconv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16526336)))]; + tensor linear_9_cast_fp16 = linear(bias = backbone_convnext_4_pwconv2_bias_to_fp16, weight = backbone_convnext_4_pwconv2_weight_to_fp16, x = input_51_cast_fp16)[name = tensor("linear_9_cast_fp16")]; + tensor backbone_convnext_4_gamma_to_fp16 = const()[name = tensor("backbone_convnext_4_gamma_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16527424)))]; + tensor x_41_cast_fp16 = mul(x = backbone_convnext_4_gamma_to_fp16, y = linear_9_cast_fp16)[name = tensor("x_41_cast_fp16")]; + tensor input_53_cast_fp16 = add(x = input_43_cast_fp16, y = x_41_cast_fp16)[name = tensor("input_53_cast_fp16")]; + tensor x_45_pad_type_0 = const()[name = tensor("x_45_pad_type_0"), val = tensor("custom")]; + tensor x_45_pad_0 = const()[name = tensor("x_45_pad_0"), val = tensor([3, 3])]; + tensor x_45_groups_0 = const()[name = tensor("x_45_groups_0"), val = tensor(512)]; + tensor x_45_strides_0 = const()[name = tensor("x_45_strides_0"), val = tensor([1])]; + tensor x_45_dilations_0 = const()[name = tensor("x_45_dilations_0"), val = tensor([1])]; + tensor transpose_9_perm_0 = const()[name = tensor("transpose_9_perm_0"), val = tensor([0, 2, 1])]; + tensor backbone_convnext_5_dwconv_weight_to_fp16 = const()[name = tensor("backbone_convnext_5_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16528512)))]; + tensor backbone_convnext_5_dwconv_bias_to_fp16 = const()[name = tensor("backbone_convnext_5_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16535744)))]; + tensor transpose_9_cast_fp16 = transpose(perm = transpose_9_perm_0, x = input_53_cast_fp16)[name = tensor("transpose_20")]; + tensor x_45_cast_fp16 = conv(bias = backbone_convnext_5_dwconv_bias_to_fp16, dilations = x_45_dilations_0, groups = x_45_groups_0, pad = x_45_pad_0, pad_type = x_45_pad_type_0, strides = x_45_strides_0, weight = backbone_convnext_5_dwconv_weight_to_fp16, x = transpose_9_cast_fp16)[name = tensor("x_45_cast_fp16")]; + tensor input_55_perm_0 = const()[name = tensor("input_55_perm_0"), val = tensor([0, 2, 1])]; + tensor input_57_axes_0 = const()[name = tensor("input_57_axes_0"), val = tensor([-1])]; + tensor backbone_convnext_5_norm_weight_to_fp16 = const()[name = tensor("backbone_convnext_5_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16536832)))]; + tensor backbone_convnext_5_norm_bias_to_fp16 = const()[name = tensor("backbone_convnext_5_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16537920)))]; + tensor input_55_cast_fp16 = transpose(perm = input_55_perm_0, x = x_45_cast_fp16)[name = tensor("transpose_19")]; + tensor input_57_cast_fp16 = layer_norm(axes = input_57_axes_0, beta = backbone_convnext_5_norm_bias_to_fp16, epsilon = var_10_to_fp16, gamma = backbone_convnext_5_norm_weight_to_fp16, x = input_55_cast_fp16)[name = tensor("input_57_cast_fp16")]; + tensor backbone_convnext_5_pwconv1_weight_to_fp16 = const()[name = tensor("backbone_convnext_5_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16539008)))]; + tensor backbone_convnext_5_pwconv1_bias_to_fp16 = const()[name = tensor("backbone_convnext_5_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18111936)))]; + tensor linear_10_cast_fp16 = linear(bias = backbone_convnext_5_pwconv1_bias_to_fp16, weight = backbone_convnext_5_pwconv1_weight_to_fp16, x = input_57_cast_fp16)[name = tensor("linear_10_cast_fp16")]; + tensor input_61_mode_0 = const()[name = tensor("input_61_mode_0"), val = tensor("EXACT")]; + tensor input_61_cast_fp16 = gelu(mode = input_61_mode_0, x = linear_10_cast_fp16)[name = tensor("input_61_cast_fp16")]; + tensor backbone_convnext_5_pwconv2_weight_to_fp16 = const()[name = tensor("backbone_convnext_5_pwconv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18115072)))]; + tensor backbone_convnext_5_pwconv2_bias_to_fp16 = const()[name = tensor("backbone_convnext_5_pwconv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19688000)))]; + tensor linear_11_cast_fp16 = linear(bias = backbone_convnext_5_pwconv2_bias_to_fp16, weight = backbone_convnext_5_pwconv2_weight_to_fp16, x = input_61_cast_fp16)[name = tensor("linear_11_cast_fp16")]; + tensor backbone_convnext_5_gamma_to_fp16 = const()[name = tensor("backbone_convnext_5_gamma_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19689088)))]; + tensor x_49_cast_fp16 = mul(x = backbone_convnext_5_gamma_to_fp16, y = linear_11_cast_fp16)[name = tensor("x_49_cast_fp16")]; + tensor input_63_cast_fp16 = add(x = input_53_cast_fp16, y = x_49_cast_fp16)[name = tensor("input_63_cast_fp16")]; + tensor x_53_pad_type_0 = const()[name = tensor("x_53_pad_type_0"), val = tensor("custom")]; + tensor x_53_pad_0 = const()[name = tensor("x_53_pad_0"), val = tensor([3, 3])]; + tensor x_53_groups_0 = const()[name = tensor("x_53_groups_0"), val = tensor(512)]; + tensor x_53_strides_0 = const()[name = tensor("x_53_strides_0"), val = tensor([1])]; + tensor x_53_dilations_0 = const()[name = tensor("x_53_dilations_0"), val = tensor([1])]; + tensor transpose_10_perm_0 = const()[name = tensor("transpose_10_perm_0"), val = tensor([0, 2, 1])]; + tensor backbone_convnext_6_dwconv_weight_to_fp16 = const()[name = tensor("backbone_convnext_6_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19690176)))]; + tensor backbone_convnext_6_dwconv_bias_to_fp16 = const()[name = tensor("backbone_convnext_6_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19697408)))]; + tensor transpose_10_cast_fp16 = transpose(perm = transpose_10_perm_0, x = input_63_cast_fp16)[name = tensor("transpose_18")]; + tensor x_53_cast_fp16 = conv(bias = backbone_convnext_6_dwconv_bias_to_fp16, dilations = x_53_dilations_0, groups = x_53_groups_0, pad = x_53_pad_0, pad_type = x_53_pad_type_0, strides = x_53_strides_0, weight = backbone_convnext_6_dwconv_weight_to_fp16, x = transpose_10_cast_fp16)[name = tensor("x_53_cast_fp16")]; + tensor input_65_perm_0 = const()[name = tensor("input_65_perm_0"), val = tensor([0, 2, 1])]; + tensor input_67_axes_0 = const()[name = tensor("input_67_axes_0"), val = tensor([-1])]; + tensor backbone_convnext_6_norm_weight_to_fp16 = const()[name = tensor("backbone_convnext_6_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19698496)))]; + tensor backbone_convnext_6_norm_bias_to_fp16 = const()[name = tensor("backbone_convnext_6_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19699584)))]; + tensor input_65_cast_fp16 = transpose(perm = input_65_perm_0, x = x_53_cast_fp16)[name = tensor("transpose_17")]; + tensor input_67_cast_fp16 = layer_norm(axes = input_67_axes_0, beta = backbone_convnext_6_norm_bias_to_fp16, epsilon = var_10_to_fp16, gamma = backbone_convnext_6_norm_weight_to_fp16, x = input_65_cast_fp16)[name = tensor("input_67_cast_fp16")]; + tensor backbone_convnext_6_pwconv1_weight_to_fp16 = const()[name = tensor("backbone_convnext_6_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19700672)))]; + tensor backbone_convnext_6_pwconv1_bias_to_fp16 = const()[name = tensor("backbone_convnext_6_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21273600)))]; + tensor linear_12_cast_fp16 = linear(bias = backbone_convnext_6_pwconv1_bias_to_fp16, weight = backbone_convnext_6_pwconv1_weight_to_fp16, x = input_67_cast_fp16)[name = tensor("linear_12_cast_fp16")]; + tensor input_71_mode_0 = const()[name = tensor("input_71_mode_0"), val = tensor("EXACT")]; + tensor input_71_cast_fp16 = gelu(mode = input_71_mode_0, x = linear_12_cast_fp16)[name = tensor("input_71_cast_fp16")]; + tensor backbone_convnext_6_pwconv2_weight_to_fp16 = const()[name = tensor("backbone_convnext_6_pwconv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21276736)))]; + tensor backbone_convnext_6_pwconv2_bias_to_fp16 = const()[name = tensor("backbone_convnext_6_pwconv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22849664)))]; + tensor linear_13_cast_fp16 = linear(bias = backbone_convnext_6_pwconv2_bias_to_fp16, weight = backbone_convnext_6_pwconv2_weight_to_fp16, x = input_71_cast_fp16)[name = tensor("linear_13_cast_fp16")]; + tensor backbone_convnext_6_gamma_to_fp16 = const()[name = tensor("backbone_convnext_6_gamma_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22850752)))]; + tensor x_57_cast_fp16 = mul(x = backbone_convnext_6_gamma_to_fp16, y = linear_13_cast_fp16)[name = tensor("x_57_cast_fp16")]; + tensor input_73_cast_fp16 = add(x = input_63_cast_fp16, y = x_57_cast_fp16)[name = tensor("input_73_cast_fp16")]; + tensor x_61_pad_type_0 = const()[name = tensor("x_61_pad_type_0"), val = tensor("custom")]; + tensor x_61_pad_0 = const()[name = tensor("x_61_pad_0"), val = tensor([3, 3])]; + tensor x_61_groups_0 = const()[name = tensor("x_61_groups_0"), val = tensor(512)]; + tensor x_61_strides_0 = const()[name = tensor("x_61_strides_0"), val = tensor([1])]; + tensor x_61_dilations_0 = const()[name = tensor("x_61_dilations_0"), val = tensor([1])]; + tensor transpose_11_perm_0 = const()[name = tensor("transpose_11_perm_0"), val = tensor([0, 2, 1])]; + tensor backbone_convnext_7_dwconv_weight_to_fp16 = const()[name = tensor("backbone_convnext_7_dwconv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22851840)))]; + tensor backbone_convnext_7_dwconv_bias_to_fp16 = const()[name = tensor("backbone_convnext_7_dwconv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22859072)))]; + tensor transpose_11_cast_fp16 = transpose(perm = transpose_11_perm_0, x = input_73_cast_fp16)[name = tensor("transpose_16")]; + tensor x_61_cast_fp16 = conv(bias = backbone_convnext_7_dwconv_bias_to_fp16, dilations = x_61_dilations_0, groups = x_61_groups_0, pad = x_61_pad_0, pad_type = x_61_pad_type_0, strides = x_61_strides_0, weight = backbone_convnext_7_dwconv_weight_to_fp16, x = transpose_11_cast_fp16)[name = tensor("x_61_cast_fp16")]; + tensor input_75_perm_0 = const()[name = tensor("input_75_perm_0"), val = tensor([0, 2, 1])]; + tensor input_77_axes_0 = const()[name = tensor("input_77_axes_0"), val = tensor([-1])]; + tensor backbone_convnext_7_norm_weight_to_fp16 = const()[name = tensor("backbone_convnext_7_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22860160)))]; + tensor backbone_convnext_7_norm_bias_to_fp16 = const()[name = tensor("backbone_convnext_7_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22861248)))]; + tensor input_75_cast_fp16 = transpose(perm = input_75_perm_0, x = x_61_cast_fp16)[name = tensor("transpose_15")]; + tensor input_77_cast_fp16 = layer_norm(axes = input_77_axes_0, beta = backbone_convnext_7_norm_bias_to_fp16, epsilon = var_10_to_fp16, gamma = backbone_convnext_7_norm_weight_to_fp16, x = input_75_cast_fp16)[name = tensor("input_77_cast_fp16")]; + tensor backbone_convnext_7_pwconv1_weight_to_fp16 = const()[name = tensor("backbone_convnext_7_pwconv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22862336)))]; + tensor backbone_convnext_7_pwconv1_bias_to_fp16 = const()[name = tensor("backbone_convnext_7_pwconv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24435264)))]; + tensor linear_14_cast_fp16 = linear(bias = backbone_convnext_7_pwconv1_bias_to_fp16, weight = backbone_convnext_7_pwconv1_weight_to_fp16, x = input_77_cast_fp16)[name = tensor("linear_14_cast_fp16")]; + tensor input_81_mode_0 = const()[name = tensor("input_81_mode_0"), val = tensor("EXACT")]; + tensor input_81_cast_fp16 = gelu(mode = input_81_mode_0, x = linear_14_cast_fp16)[name = tensor("input_81_cast_fp16")]; + tensor backbone_convnext_7_pwconv2_weight_to_fp16 = const()[name = tensor("backbone_convnext_7_pwconv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24438400)))]; + tensor backbone_convnext_7_pwconv2_bias_to_fp16 = const()[name = tensor("backbone_convnext_7_pwconv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26011328)))]; + tensor linear_15_cast_fp16 = linear(bias = backbone_convnext_7_pwconv2_bias_to_fp16, weight = backbone_convnext_7_pwconv2_weight_to_fp16, x = input_81_cast_fp16)[name = tensor("linear_15_cast_fp16")]; + tensor backbone_convnext_7_gamma_to_fp16 = const()[name = tensor("backbone_convnext_7_gamma_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26012416)))]; + tensor x_65_cast_fp16 = mul(x = backbone_convnext_7_gamma_to_fp16, y = linear_15_cast_fp16)[name = tensor("x_65_cast_fp16")]; + tensor x_69_cast_fp16 = add(x = input_73_cast_fp16, y = x_65_cast_fp16)[name = tensor("x_69_cast_fp16")]; + tensor features_axes_0 = const()[name = tensor("features_axes_0"), val = tensor([-1])]; + tensor backbone_final_layer_norm_weight_to_fp16 = const()[name = tensor("backbone_final_layer_norm_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26013504)))]; + tensor backbone_final_layer_norm_bias_to_fp16 = const()[name = tensor("backbone_final_layer_norm_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26014592)))]; + tensor features_cast_fp16 = layer_norm(axes = features_axes_0, beta = backbone_final_layer_norm_bias_to_fp16, epsilon = var_10_to_fp16, gamma = backbone_final_layer_norm_weight_to_fp16, x = x_69_cast_fp16)[name = tensor("features_cast_fp16")]; + tensor input_85_perm_0 = const()[name = tensor("input_85_perm_0"), val = tensor([0, 2, 1])]; + tensor input_87_pad_type_0 = const()[name = tensor("input_87_pad_type_0"), val = tensor("custom")]; + tensor input_87_pad_0 = const()[name = tensor("input_87_pad_0"), val = tensor([3, 3])]; + tensor input_87_strides_0 = const()[name = tensor("input_87_strides_0"), val = tensor([2])]; + tensor input_87_dilations_0 = const()[name = tensor("input_87_dilations_0"), val = tensor([1])]; + tensor input_87_groups_0 = const()[name = tensor("input_87_groups_0"), val = tensor(1)]; + tensor input_87_has_output_shape_output_shape_0 = const()[name = tensor("input_87_has_output_shape_output_shape_0"), val = tensor([1, 256, 564])]; + tensor upsampler_upsample_layers_0_weight_to_fp16 = const()[name = tensor("upsampler_upsample_layers_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(26015680)))]; + tensor upsampler_upsample_layers_0_bias_to_fp16 = const()[name = tensor("upsampler_upsample_layers_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28112896)))]; + tensor input_85_cast_fp16 = transpose(perm = input_85_perm_0, x = features_cast_fp16)[name = tensor("transpose_14")]; + tensor input_87_has_output_shape_cast_fp16 = conv_transpose(bias = upsampler_upsample_layers_0_bias_to_fp16, dilations = input_87_dilations_0, groups = input_87_groups_0, output_shape = input_87_has_output_shape_output_shape_0, pad = input_87_pad_0, pad_type = input_87_pad_type_0, strides = input_87_strides_0, weight = upsampler_upsample_layers_0_weight_to_fp16, x = input_85_cast_fp16)[name = tensor("input_87_has_output_shape_cast_fp16")]; + tensor reshape_0_shape_0 = const()[name = tensor("reshape_0_shape_0"), val = tensor([1, 32, 8, 564])]; + tensor reshape_0_cast_fp16 = reshape(shape = reshape_0_shape_0, x = input_87_has_output_shape_cast_fp16)[name = tensor("reshape_0_cast_fp16")]; + tensor reduce_mean_0_axes_0 = const()[name = tensor("reduce_mean_0_axes_0"), val = tensor([2, 3])]; + tensor reduce_mean_0_keep_dims_0 = const()[name = tensor("reduce_mean_0_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_0_cast_fp16 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0_cast_fp16)[name = tensor("reduce_mean_0_cast_fp16")]; + tensor sub_0_cast_fp16 = sub(x = reshape_0_cast_fp16, y = reduce_mean_0_cast_fp16)[name = tensor("sub_0_cast_fp16")]; + tensor square_0_cast_fp16 = square(x = sub_0_cast_fp16)[name = tensor("square_0_cast_fp16")]; + tensor reduce_mean_2_axes_0 = const()[name = tensor("reduce_mean_2_axes_0"), val = tensor([2, 3])]; + tensor reduce_mean_2_keep_dims_0 = const()[name = tensor("reduce_mean_2_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_2_cast_fp16 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0_cast_fp16)[name = tensor("reduce_mean_2_cast_fp16")]; + tensor add_0_y_0_to_fp16 = const()[name = tensor("add_0_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_0_cast_fp16 = add(x = reduce_mean_2_cast_fp16, y = add_0_y_0_to_fp16)[name = tensor("add_0_cast_fp16")]; + tensor sqrt_0_cast_fp16 = sqrt(x = add_0_cast_fp16)[name = tensor("sqrt_0_cast_fp16")]; + tensor real_div_0_cast_fp16 = real_div(x = sub_0_cast_fp16, y = sqrt_0_cast_fp16)[name = tensor("real_div_0_cast_fp16")]; + tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([1, 256, 564])]; + tensor reshape_1_cast_fp16 = reshape(shape = reshape_1_shape_0, x = real_div_0_cast_fp16)[name = tensor("reshape_1_cast_fp16")]; + tensor reshape_2_to_fp16 = const()[name = tensor("reshape_2_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28113472)))]; + tensor mul_0_cast_fp16 = mul(x = reshape_1_cast_fp16, y = reshape_2_to_fp16)[name = tensor("mul_0_cast_fp16")]; + tensor reshape_3_to_fp16 = const()[name = tensor("reshape_3_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28114048)))]; + tensor add_1_cast_fp16 = add(x = mul_0_cast_fp16, y = reshape_3_to_fp16)[name = tensor("add_1_cast_fp16")]; + tensor upsampler_resnet_blocks_0_snake1_alpha_to_fp16 = const()[name = tensor("upsampler_resnet_blocks_0_snake1_alpha_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28114624)))]; + tensor var_312_cast_fp16 = mul(x = upsampler_resnet_blocks_0_snake1_alpha_to_fp16, y = add_1_cast_fp16)[name = tensor("op_312_cast_fp16")]; + tensor var_313_cast_fp16 = sin(x = var_312_cast_fp16)[name = tensor("op_313_cast_fp16")]; + tensor var_276_promoted_to_fp16 = const()[name = tensor("op_276_promoted_to_fp16"), val = tensor(0x1p+1)]; + tensor var_314_cast_fp16 = pow(x = var_313_cast_fp16, y = var_276_promoted_to_fp16)[name = tensor("op_314_cast_fp16")]; + tensor var_311_to_fp16 = const()[name = tensor("op_311_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28115200)))]; + tensor var_315_cast_fp16 = mul(x = var_311_to_fp16, y = var_314_cast_fp16)[name = tensor("op_315_cast_fp16")]; + tensor input_89_cast_fp16 = add(x = add_1_cast_fp16, y = var_315_cast_fp16)[name = tensor("input_89_cast_fp16")]; + tensor input_91_pad_type_0 = const()[name = tensor("input_91_pad_type_0"), val = tensor("custom")]; + tensor input_91_pad_0 = const()[name = tensor("input_91_pad_0"), val = tensor([1, 1])]; + tensor input_91_strides_0 = const()[name = tensor("input_91_strides_0"), val = tensor([1])]; + tensor input_91_dilations_0 = const()[name = tensor("input_91_dilations_0"), val = tensor([1])]; + tensor input_91_groups_0 = const()[name = tensor("input_91_groups_0"), val = tensor(1)]; + tensor upsampler_resnet_blocks_0_conv1_weight_to_fp16 = const()[name = tensor("upsampler_resnet_blocks_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28115776)))]; + tensor upsampler_resnet_blocks_0_conv1_bias_to_fp16 = const()[name = tensor("upsampler_resnet_blocks_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28509056)))]; + tensor input_91_cast_fp16 = conv(bias = upsampler_resnet_blocks_0_conv1_bias_to_fp16, dilations = input_91_dilations_0, groups = input_91_groups_0, pad = input_91_pad_0, pad_type = input_91_pad_type_0, strides = input_91_strides_0, weight = upsampler_resnet_blocks_0_conv1_weight_to_fp16, x = input_89_cast_fp16)[name = tensor("input_91_cast_fp16")]; + tensor reshape_4_shape_0 = const()[name = tensor("reshape_4_shape_0"), val = tensor([1, 32, 8, 564])]; + tensor reshape_4_cast_fp16 = reshape(shape = reshape_4_shape_0, x = input_91_cast_fp16)[name = tensor("reshape_4_cast_fp16")]; + tensor reduce_mean_3_axes_0 = const()[name = tensor("reduce_mean_3_axes_0"), val = tensor([2, 3])]; + tensor reduce_mean_3_keep_dims_0 = const()[name = tensor("reduce_mean_3_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_3_cast_fp16 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4_cast_fp16)[name = tensor("reduce_mean_3_cast_fp16")]; + tensor sub_2_cast_fp16 = sub(x = reshape_4_cast_fp16, y = reduce_mean_3_cast_fp16)[name = tensor("sub_2_cast_fp16")]; + tensor square_1_cast_fp16 = square(x = sub_2_cast_fp16)[name = tensor("square_1_cast_fp16")]; + tensor reduce_mean_5_axes_0 = const()[name = tensor("reduce_mean_5_axes_0"), val = tensor([2, 3])]; + tensor reduce_mean_5_keep_dims_0 = const()[name = tensor("reduce_mean_5_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_5_cast_fp16 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1_cast_fp16)[name = tensor("reduce_mean_5_cast_fp16")]; + tensor add_2_y_0_to_fp16 = const()[name = tensor("add_2_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_2_cast_fp16 = add(x = reduce_mean_5_cast_fp16, y = add_2_y_0_to_fp16)[name = tensor("add_2_cast_fp16")]; + tensor sqrt_1_cast_fp16 = sqrt(x = add_2_cast_fp16)[name = tensor("sqrt_1_cast_fp16")]; + tensor real_div_1_cast_fp16 = real_div(x = sub_2_cast_fp16, y = sqrt_1_cast_fp16)[name = tensor("real_div_1_cast_fp16")]; + tensor reshape_5_shape_0 = const()[name = tensor("reshape_5_shape_0"), val = tensor([1, 256, 564])]; + tensor reshape_5_cast_fp16 = reshape(shape = reshape_5_shape_0, x = real_div_1_cast_fp16)[name = tensor("reshape_5_cast_fp16")]; + tensor reshape_6_to_fp16 = const()[name = tensor("reshape_6_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28509632)))]; + tensor mul_1_cast_fp16 = mul(x = reshape_5_cast_fp16, y = reshape_6_to_fp16)[name = tensor("mul_1_cast_fp16")]; + tensor reshape_7_to_fp16 = const()[name = tensor("reshape_7_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28510208)))]; + tensor add_3_cast_fp16 = add(x = mul_1_cast_fp16, y = reshape_7_to_fp16)[name = tensor("add_3_cast_fp16")]; + tensor upsampler_resnet_blocks_0_snake2_alpha_to_fp16 = const()[name = tensor("upsampler_resnet_blocks_0_snake2_alpha_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28510784)))]; + tensor var_331_cast_fp16 = mul(x = upsampler_resnet_blocks_0_snake2_alpha_to_fp16, y = add_3_cast_fp16)[name = tensor("op_331_cast_fp16")]; + tensor var_332_cast_fp16 = sin(x = var_331_cast_fp16)[name = tensor("op_332_cast_fp16")]; + tensor var_276_promoted_1_to_fp16 = const()[name = tensor("op_276_promoted_1_to_fp16"), val = tensor(0x1p+1)]; + tensor var_333_cast_fp16 = pow(x = var_332_cast_fp16, y = var_276_promoted_1_to_fp16)[name = tensor("op_333_cast_fp16")]; + tensor var_330_to_fp16 = const()[name = tensor("op_330_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28511360)))]; + tensor var_334_cast_fp16 = mul(x = var_330_to_fp16, y = var_333_cast_fp16)[name = tensor("op_334_cast_fp16")]; + tensor input_93_cast_fp16 = add(x = add_3_cast_fp16, y = var_334_cast_fp16)[name = tensor("input_93_cast_fp16")]; + tensor h_1_pad_type_0 = const()[name = tensor("h_1_pad_type_0"), val = tensor("custom")]; + tensor h_1_pad_0 = const()[name = tensor("h_1_pad_0"), val = tensor([1, 1])]; + tensor h_1_strides_0 = const()[name = tensor("h_1_strides_0"), val = tensor([1])]; + tensor h_1_dilations_0 = const()[name = tensor("h_1_dilations_0"), val = tensor([1])]; + tensor h_1_groups_0 = const()[name = tensor("h_1_groups_0"), val = tensor(1)]; + tensor upsampler_resnet_blocks_0_conv2_weight_to_fp16 = const()[name = tensor("upsampler_resnet_blocks_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28511936)))]; + tensor upsampler_resnet_blocks_0_conv2_bias_to_fp16 = const()[name = tensor("upsampler_resnet_blocks_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28905216)))]; + tensor h_1_cast_fp16 = conv(bias = upsampler_resnet_blocks_0_conv2_bias_to_fp16, dilations = h_1_dilations_0, groups = h_1_groups_0, pad = h_1_pad_0, pad_type = h_1_pad_type_0, strides = h_1_strides_0, weight = upsampler_resnet_blocks_0_conv2_weight_to_fp16, x = input_93_cast_fp16)[name = tensor("h_1_cast_fp16")]; + tensor input_97_cast_fp16 = add(x = input_87_has_output_shape_cast_fp16, y = h_1_cast_fp16)[name = tensor("input_97_cast_fp16")]; + tensor input_99_pad_type_0 = const()[name = tensor("input_99_pad_type_0"), val = tensor("custom")]; + tensor input_99_pad_0 = const()[name = tensor("input_99_pad_0"), val = tensor([3, 3])]; + tensor input_99_strides_0 = const()[name = tensor("input_99_strides_0"), val = tensor([1])]; + tensor input_99_dilations_0 = const()[name = tensor("input_99_dilations_0"), val = tensor([1])]; + tensor input_99_groups_0 = const()[name = tensor("input_99_groups_0"), val = tensor(1)]; + tensor input_99_has_output_shape_output_shape_0 = const()[name = tensor("input_99_has_output_shape_output_shape_0"), val = tensor([1, 128, 565])]; + tensor upsampler_upsample_layers_1_weight_to_fp16 = const()[name = tensor("upsampler_upsample_layers_1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28905792)))]; + tensor upsampler_upsample_layers_1_bias_to_fp16 = const()[name = tensor("upsampler_upsample_layers_1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29430144)))]; + tensor input_99_has_output_shape_cast_fp16 = conv_transpose(bias = upsampler_upsample_layers_1_bias_to_fp16, dilations = input_99_dilations_0, groups = input_99_groups_0, output_shape = input_99_has_output_shape_output_shape_0, pad = input_99_pad_0, pad_type = input_99_pad_type_0, strides = input_99_strides_0, weight = upsampler_upsample_layers_1_weight_to_fp16, x = input_97_cast_fp16)[name = tensor("input_99_has_output_shape_cast_fp16")]; + tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([1, 32, 4, 565])]; + tensor reshape_8_cast_fp16 = reshape(shape = reshape_8_shape_0, x = input_99_has_output_shape_cast_fp16)[name = tensor("reshape_8_cast_fp16")]; + tensor reduce_mean_6_axes_0 = const()[name = tensor("reduce_mean_6_axes_0"), val = tensor([2, 3])]; + tensor reduce_mean_6_keep_dims_0 = const()[name = tensor("reduce_mean_6_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_6_cast_fp16 = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8_cast_fp16)[name = tensor("reduce_mean_6_cast_fp16")]; + tensor sub_4_cast_fp16 = sub(x = reshape_8_cast_fp16, y = reduce_mean_6_cast_fp16)[name = tensor("sub_4_cast_fp16")]; + tensor square_2_cast_fp16 = square(x = sub_4_cast_fp16)[name = tensor("square_2_cast_fp16")]; + tensor reduce_mean_8_axes_0 = const()[name = tensor("reduce_mean_8_axes_0"), val = tensor([2, 3])]; + tensor reduce_mean_8_keep_dims_0 = const()[name = tensor("reduce_mean_8_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_8_cast_fp16 = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2_cast_fp16)[name = tensor("reduce_mean_8_cast_fp16")]; + tensor add_4_y_0_to_fp16 = const()[name = tensor("add_4_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_4_cast_fp16 = add(x = reduce_mean_8_cast_fp16, y = add_4_y_0_to_fp16)[name = tensor("add_4_cast_fp16")]; + tensor sqrt_2_cast_fp16 = sqrt(x = add_4_cast_fp16)[name = tensor("sqrt_2_cast_fp16")]; + tensor real_div_2_cast_fp16 = real_div(x = sub_4_cast_fp16, y = sqrt_2_cast_fp16)[name = tensor("real_div_2_cast_fp16")]; + tensor reshape_9_shape_0 = const()[name = tensor("reshape_9_shape_0"), val = tensor([1, 128, 565])]; + tensor reshape_9_cast_fp16 = reshape(shape = reshape_9_shape_0, x = real_div_2_cast_fp16)[name = tensor("reshape_9_cast_fp16")]; + tensor reshape_10_to_fp16 = const()[name = tensor("reshape_10_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29430464)))]; + tensor mul_2_cast_fp16 = mul(x = reshape_9_cast_fp16, y = reshape_10_to_fp16)[name = tensor("mul_2_cast_fp16")]; + tensor reshape_11_to_fp16 = const()[name = tensor("reshape_11_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29430784)))]; + tensor add_5_cast_fp16 = add(x = mul_2_cast_fp16, y = reshape_11_to_fp16)[name = tensor("add_5_cast_fp16")]; + tensor upsampler_resnet_blocks_1_snake1_alpha_to_fp16 = const()[name = tensor("upsampler_resnet_blocks_1_snake1_alpha_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29431104)))]; + tensor var_365_cast_fp16 = mul(x = upsampler_resnet_blocks_1_snake1_alpha_to_fp16, y = add_5_cast_fp16)[name = tensor("op_365_cast_fp16")]; + tensor var_366_cast_fp16 = sin(x = var_365_cast_fp16)[name = tensor("op_366_cast_fp16")]; + tensor var_276_promoted_2_to_fp16 = const()[name = tensor("op_276_promoted_2_to_fp16"), val = tensor(0x1p+1)]; + tensor var_367_cast_fp16 = pow(x = var_366_cast_fp16, y = var_276_promoted_2_to_fp16)[name = tensor("op_367_cast_fp16")]; + tensor var_364_to_fp16 = const()[name = tensor("op_364_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29431424)))]; + tensor var_368_cast_fp16 = mul(x = var_364_to_fp16, y = var_367_cast_fp16)[name = tensor("op_368_cast_fp16")]; + tensor input_101_cast_fp16 = add(x = add_5_cast_fp16, y = var_368_cast_fp16)[name = tensor("input_101_cast_fp16")]; + tensor input_103_pad_type_0 = const()[name = tensor("input_103_pad_type_0"), val = tensor("custom")]; + tensor input_103_pad_0 = const()[name = tensor("input_103_pad_0"), val = tensor([1, 1])]; + tensor input_103_strides_0 = const()[name = tensor("input_103_strides_0"), val = tensor([1])]; + tensor input_103_dilations_0 = const()[name = tensor("input_103_dilations_0"), val = tensor([1])]; + tensor input_103_groups_0 = const()[name = tensor("input_103_groups_0"), val = tensor(1)]; + tensor upsampler_resnet_blocks_1_conv1_weight_to_fp16 = const()[name = tensor("upsampler_resnet_blocks_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29431744)))]; + tensor upsampler_resnet_blocks_1_conv1_bias_to_fp16 = const()[name = tensor("upsampler_resnet_blocks_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29530112)))]; + tensor input_103_cast_fp16 = conv(bias = upsampler_resnet_blocks_1_conv1_bias_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 = upsampler_resnet_blocks_1_conv1_weight_to_fp16, x = input_101_cast_fp16)[name = tensor("input_103_cast_fp16")]; + tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([1, 32, 4, 565])]; + tensor reshape_12_cast_fp16 = reshape(shape = reshape_12_shape_0, x = input_103_cast_fp16)[name = tensor("reshape_12_cast_fp16")]; + tensor reduce_mean_9_axes_0 = const()[name = tensor("reduce_mean_9_axes_0"), val = tensor([2, 3])]; + tensor reduce_mean_9_keep_dims_0 = const()[name = tensor("reduce_mean_9_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_9_cast_fp16 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12_cast_fp16)[name = tensor("reduce_mean_9_cast_fp16")]; + tensor sub_6_cast_fp16 = sub(x = reshape_12_cast_fp16, y = reduce_mean_9_cast_fp16)[name = tensor("sub_6_cast_fp16")]; + tensor square_3_cast_fp16 = square(x = sub_6_cast_fp16)[name = tensor("square_3_cast_fp16")]; + tensor reduce_mean_11_axes_0 = const()[name = tensor("reduce_mean_11_axes_0"), val = tensor([2, 3])]; + tensor reduce_mean_11_keep_dims_0 = const()[name = tensor("reduce_mean_11_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_11_cast_fp16 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3_cast_fp16)[name = tensor("reduce_mean_11_cast_fp16")]; + tensor add_6_y_0_to_fp16 = const()[name = tensor("add_6_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_6_cast_fp16 = add(x = reduce_mean_11_cast_fp16, y = add_6_y_0_to_fp16)[name = tensor("add_6_cast_fp16")]; + tensor sqrt_3_cast_fp16 = sqrt(x = add_6_cast_fp16)[name = tensor("sqrt_3_cast_fp16")]; + tensor real_div_3_cast_fp16 = real_div(x = sub_6_cast_fp16, y = sqrt_3_cast_fp16)[name = tensor("real_div_3_cast_fp16")]; + tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([1, 128, 565])]; + tensor reshape_13_cast_fp16 = reshape(shape = reshape_13_shape_0, x = real_div_3_cast_fp16)[name = tensor("reshape_13_cast_fp16")]; + tensor reshape_14_to_fp16 = const()[name = tensor("reshape_14_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29530432)))]; + tensor mul_3_cast_fp16 = mul(x = reshape_13_cast_fp16, y = reshape_14_to_fp16)[name = tensor("mul_3_cast_fp16")]; + tensor reshape_15_to_fp16 = const()[name = tensor("reshape_15_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29530752)))]; + tensor add_7_cast_fp16 = add(x = mul_3_cast_fp16, y = reshape_15_to_fp16)[name = tensor("add_7_cast_fp16")]; + tensor upsampler_resnet_blocks_1_snake2_alpha_to_fp16 = const()[name = tensor("upsampler_resnet_blocks_1_snake2_alpha_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29531072)))]; + tensor var_384_cast_fp16 = mul(x = upsampler_resnet_blocks_1_snake2_alpha_to_fp16, y = add_7_cast_fp16)[name = tensor("op_384_cast_fp16")]; + tensor var_385_cast_fp16 = sin(x = var_384_cast_fp16)[name = tensor("op_385_cast_fp16")]; + tensor var_276_promoted_3_to_fp16 = const()[name = tensor("op_276_promoted_3_to_fp16"), val = tensor(0x1p+1)]; + tensor var_386_cast_fp16 = pow(x = var_385_cast_fp16, y = var_276_promoted_3_to_fp16)[name = tensor("op_386_cast_fp16")]; + tensor var_383_to_fp16 = const()[name = tensor("op_383_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29531392)))]; + tensor var_387_cast_fp16 = mul(x = var_383_to_fp16, y = var_386_cast_fp16)[name = tensor("op_387_cast_fp16")]; + tensor input_105_cast_fp16 = add(x = add_7_cast_fp16, y = var_387_cast_fp16)[name = tensor("input_105_cast_fp16")]; + tensor h_pad_type_0 = const()[name = tensor("h_pad_type_0"), val = tensor("custom")]; + tensor h_pad_0 = const()[name = tensor("h_pad_0"), val = tensor([1, 1])]; + tensor h_strides_0 = const()[name = tensor("h_strides_0"), val = tensor([1])]; + tensor h_dilations_0 = const()[name = tensor("h_dilations_0"), val = tensor([1])]; + tensor h_groups_0 = const()[name = tensor("h_groups_0"), val = tensor(1)]; + tensor upsampler_resnet_blocks_1_conv2_weight_to_fp16 = const()[name = tensor("upsampler_resnet_blocks_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29531712)))]; + tensor upsampler_resnet_blocks_1_conv2_bias_to_fp16 = const()[name = tensor("upsampler_resnet_blocks_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29630080)))]; + tensor h_cast_fp16 = conv(bias = upsampler_resnet_blocks_1_conv2_bias_to_fp16, dilations = h_dilations_0, groups = h_groups_0, pad = h_pad_0, pad_type = h_pad_type_0, strides = h_strides_0, weight = upsampler_resnet_blocks_1_conv2_weight_to_fp16, x = input_105_cast_fp16)[name = tensor("h_cast_fp16")]; + tensor x_79_cast_fp16 = add(x = input_99_has_output_shape_cast_fp16, y = h_cast_fp16)[name = tensor("x_79_cast_fp16")]; + tensor input_109_perm_0 = const()[name = tensor("input_109_perm_0"), val = tensor([0, 2, 1])]; + tensor upsampler_out_proj_weight_to_fp16 = const()[name = tensor("upsampler_out_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29630400)))]; + tensor upsampler_out_proj_bias_to_fp16 = const()[name = tensor("upsampler_out_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29761536)))]; + tensor input_109_cast_fp16 = transpose(perm = input_109_perm_0, x = x_79_cast_fp16)[name = tensor("transpose_13")]; + tensor linear_16_cast_fp16 = linear(bias = upsampler_out_proj_bias_to_fp16, weight = upsampler_out_proj_weight_to_fp16, x = input_109_cast_fp16)[name = tensor("linear_16_cast_fp16")]; + tensor const_11_to_fp16 = const()[name = tensor("const_11_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29762624)))]; + tensor var_407_cast_fp16 = mul(x = const_11_to_fp16, y = linear_16_cast_fp16)[name = tensor("op_407_cast_fp16")]; + tensor var_408_cast_fp16 = sin(x = var_407_cast_fp16)[name = tensor("op_408_cast_fp16")]; + tensor var_276_promoted_4_to_fp16 = const()[name = tensor("op_276_promoted_4_to_fp16"), val = tensor(0x1p+1)]; + tensor var_409_cast_fp16 = pow(x = var_408_cast_fp16, y = var_276_promoted_4_to_fp16)[name = tensor("op_409_cast_fp16")]; + tensor const_12_to_fp16 = const()[name = tensor("const_12_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29763712)))]; + tensor var_410_cast_fp16 = mul(x = const_12_to_fp16, y = var_409_cast_fp16)[name = tensor("op_410_cast_fp16")]; + tensor upsampled_cast_fp16 = add(x = linear_16_cast_fp16, y = var_410_cast_fp16)[name = tensor("upsampled_cast_fp16")]; + tensor head_48k_out_weight_to_fp16 = const()[name = tensor("head_48k_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29764800)))]; + tensor head_48k_out_bias_to_fp16 = const()[name = tensor("head_48k_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30815488)))]; + tensor linear_17_cast_fp16 = linear(bias = head_48k_out_bias_to_fp16, weight = head_48k_out_weight_to_fp16, x = upsampled_cast_fp16)[name = tensor("linear_17_cast_fp16")]; + tensor var_438_begin_0 = const()[name = tensor("op_438_begin_0"), val = tensor([0, 0, 0])]; + tensor var_438_end_0 = const()[name = tensor("op_438_end_0"), val = tensor([1, 565, 513])]; + tensor var_438_end_mask_0 = const()[name = tensor("op_438_end_mask_0"), val = tensor([true, true, false])]; + tensor var_438_cast_fp16 = slice_by_index(begin = var_438_begin_0, end = var_438_end_0, end_mask = var_438_end_mask_0, x = linear_17_cast_fp16)[name = tensor("op_438_cast_fp16")]; + tensor const_0_to_fp16 = const()[name = tensor("const_0_to_fp16"), val = tensor(-inf)]; + tensor var_422_to_fp16 = const()[name = tensor("op_422_to_fp16"), val = tensor(0x1.26cp+2)]; + tensor clip_0_cast_fp16 = clip(alpha = const_0_to_fp16, beta = var_422_to_fp16, x = var_438_cast_fp16)[name = tensor("clip_0_cast_fp16")]; + tensor phase_1_begin_0 = const()[name = tensor("phase_1_begin_0"), val = tensor([0, 0, 513])]; + tensor phase_1_end_0 = const()[name = tensor("phase_1_end_0"), val = tensor([1, 565, 1026])]; + tensor phase_1_end_mask_0 = const()[name = tensor("phase_1_end_mask_0"), val = tensor([true, true, true])]; + tensor phase_1_cast_fp16 = slice_by_index(begin = phase_1_begin_0, end = phase_1_end_0, end_mask = phase_1_end_mask_0, x = linear_17_cast_fp16)[name = tensor("phase_1_cast_fp16")]; + tensor mag_1_cast_fp16 = exp(x = clip_0_cast_fp16)[name = tensor("mag_1_cast_fp16")]; + tensor var_444_cast_fp16 = cos(x = phase_1_cast_fp16)[name = tensor("op_444_cast_fp16")]; + tensor real_1_cast_fp16 = mul(x = mag_1_cast_fp16, y = var_444_cast_fp16)[name = tensor("real_1_cast_fp16")]; + tensor var_446_cast_fp16 = sin(x = phase_1_cast_fp16)[name = tensor("op_446_cast_fp16")]; + tensor imag_1_cast_fp16 = mul(x = mag_1_cast_fp16, y = var_446_cast_fp16)[name = tensor("imag_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(30817664)))]; + tensor var_448_bias_0_to_fp16 = const()[name = tensor("op_448_bias_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(31868352)))]; + tensor var_448_cast_fp16 = linear(bias = var_448_bias_0_to_fp16, weight = transpose_0_to_fp16, x = real_1_cast_fp16)[name = tensor("op_448_cast_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(31870464)))]; + tensor var_449_cast_fp16 = linear(bias = var_448_bias_0_to_fp16, weight = transpose_1_to_fp16, x = imag_1_cast_fp16)[name = tensor("op_449_cast_fp16")]; + tensor frames_1_cast_fp16 = add(x = var_448_cast_fp16, y = var_449_cast_fp16)[name = tensor("frames_1_cast_fp16")]; + tensor var_453_begin_0 = const()[name = tensor("op_453_begin_0"), val = tensor([0, 0, 0])]; + tensor var_453_end_0 = const()[name = tensor("op_453_end_0"), val = tensor([1, 565, 256])]; + tensor var_453_end_mask_0 = const()[name = tensor("op_453_end_mask_0"), val = tensor([true, true, false])]; + tensor var_453_cast_fp16 = slice_by_index(begin = var_453_begin_0, end = var_453_end_0, end_mask = var_453_end_mask_0, x = frames_1_cast_fp16)[name = tensor("op_453_cast_fp16")]; + tensor var_454 = const()[name = tensor("op_454"), val = tensor([1, 144640])]; + tensor input_113_cast_fp16 = reshape(shape = var_454, x = var_453_cast_fp16)[name = tensor("input_113_cast_fp16")]; + tensor y_1_pad_0 = const()[name = tensor("y_1_pad_0"), val = tensor([0, 0, 0, 768])]; + tensor y_1_mode_0 = const()[name = tensor("y_1_mode_0"), val = tensor("constant")]; + tensor const_1_to_fp16 = const()[name = tensor("const_1_to_fp16"), val = tensor(0x0p+0)]; + tensor y_1_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = y_1_mode_0, pad = y_1_pad_0, x = input_113_cast_fp16)[name = tensor("y_1_cast_fp16")]; + tensor var_460_begin_0 = const()[name = tensor("op_460_begin_0"), val = tensor([0, 0, 256])]; + tensor var_460_end_0 = const()[name = tensor("op_460_end_0"), val = tensor([1, 565, 512])]; + tensor var_460_end_mask_0 = const()[name = tensor("op_460_end_mask_0"), val = tensor([true, true, false])]; + tensor var_460_cast_fp16 = slice_by_index(begin = var_460_begin_0, end = var_460_end_0, end_mask = var_460_end_mask_0, x = frames_1_cast_fp16)[name = tensor("op_460_cast_fp16")]; + tensor var_461 = const()[name = tensor("op_461"), val = tensor([1, 144640])]; + tensor input_115_cast_fp16 = reshape(shape = var_461, x = var_460_cast_fp16)[name = tensor("input_115_cast_fp16")]; + tensor part_1_pad_0 = const()[name = tensor("part_1_pad_0"), val = tensor([0, 0, 256, 512])]; + tensor part_1_mode_0 = const()[name = tensor("part_1_mode_0"), val = tensor("constant")]; + tensor const_2_to_fp16 = const()[name = tensor("const_2_to_fp16"), val = tensor(0x0p+0)]; + tensor part_1_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = part_1_mode_0, pad = part_1_pad_0, x = input_115_cast_fp16)[name = tensor("part_1_cast_fp16")]; + tensor y_3_cast_fp16 = add(x = y_1_cast_fp16, y = part_1_cast_fp16)[name = tensor("y_3_cast_fp16")]; + tensor var_468_begin_0 = const()[name = tensor("op_468_begin_0"), val = tensor([0, 0, 512])]; + tensor var_468_end_0 = const()[name = tensor("op_468_end_0"), val = tensor([1, 565, 768])]; + tensor var_468_end_mask_0 = const()[name = tensor("op_468_end_mask_0"), val = tensor([true, true, false])]; + tensor var_468_cast_fp16 = slice_by_index(begin = var_468_begin_0, end = var_468_end_0, end_mask = var_468_end_mask_0, x = frames_1_cast_fp16)[name = tensor("op_468_cast_fp16")]; + tensor var_469 = const()[name = tensor("op_469"), val = tensor([1, 144640])]; + tensor input_117_cast_fp16 = reshape(shape = var_469, x = var_468_cast_fp16)[name = tensor("input_117_cast_fp16")]; + tensor part_3_pad_0 = const()[name = tensor("part_3_pad_0"), val = tensor([0, 0, 512, 256])]; + tensor part_3_mode_0 = const()[name = tensor("part_3_mode_0"), val = tensor("constant")]; + tensor const_3_to_fp16 = const()[name = tensor("const_3_to_fp16"), val = tensor(0x0p+0)]; + tensor part_3_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = part_3_mode_0, pad = part_3_pad_0, x = input_117_cast_fp16)[name = tensor("part_3_cast_fp16")]; + tensor y_5_cast_fp16 = add(x = y_3_cast_fp16, y = part_3_cast_fp16)[name = tensor("y_5_cast_fp16")]; + tensor var_476_begin_0 = const()[name = tensor("op_476_begin_0"), val = tensor([0, 0, 768])]; + tensor var_476_end_0 = const()[name = tensor("op_476_end_0"), val = tensor([1, 565, 1])]; + tensor var_476_end_mask_0 = const()[name = tensor("op_476_end_mask_0"), val = tensor([true, true, true])]; + tensor var_476_cast_fp16 = slice_by_index(begin = var_476_begin_0, end = var_476_end_0, end_mask = var_476_end_mask_0, x = frames_1_cast_fp16)[name = tensor("op_476_cast_fp16")]; + tensor var_477 = const()[name = tensor("op_477"), val = tensor([1, 144640])]; + tensor input_119_cast_fp16 = reshape(shape = var_477, x = var_476_cast_fp16)[name = tensor("input_119_cast_fp16")]; + tensor part_5_pad_0 = const()[name = tensor("part_5_pad_0"), val = tensor([0, 0, 768, 0])]; + tensor part_5_mode_0 = const()[name = tensor("part_5_mode_0"), val = tensor("constant")]; + tensor const_4_to_fp16 = const()[name = tensor("const_4_to_fp16"), val = tensor(0x0p+0)]; + tensor part_5_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = part_5_mode_0, pad = part_5_pad_0, x = input_119_cast_fp16)[name = tensor("part_5_cast_fp16")]; + tensor y_7_cast_fp16 = add(x = y_5_cast_fp16, y = part_5_cast_fp16)[name = tensor("y_7_cast_fp16")]; + tensor y_9_begin_0 = const()[name = tensor("y_9_begin_0"), val = tensor([0, 512])]; + tensor y_9_end_0 = const()[name = tensor("y_9_end_0"), val = tensor([1, 144896])]; + tensor y_9_end_mask_0 = const()[name = tensor("y_9_end_mask_0"), val = tensor([true, false])]; + tensor y_9_cast_fp16 = slice_by_index(begin = y_9_begin_0, end = y_9_end_0, end_mask = y_9_end_mask_0, x = y_7_cast_fp16)[name = tensor("y_9_cast_fp16")]; + tensor head_48k_env_inv_to_fp16 = const()[name = tensor("head_48k_env_inv_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32921152)))]; + tensor audio_hi_cast_fp16 = mul(x = y_9_cast_fp16, y = head_48k_env_inv_to_fp16)[name = tensor("audio_hi_cast_fp16")]; + tensor head_24k_out_weight_to_fp16 = const()[name = tensor("head_24k_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33209984)))]; + tensor head_24k_out_bias_to_fp16 = const()[name = tensor("head_24k_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34260672)))]; + tensor linear_18_cast_fp16 = linear(bias = head_24k_out_bias_to_fp16, weight = head_24k_out_weight_to_fp16, x = features_cast_fp16)[name = tensor("linear_18_cast_fp16")]; + tensor var_508_begin_0 = const()[name = tensor("op_508_begin_0"), val = tensor([0, 0, 0])]; + tensor var_508_end_0 = const()[name = tensor("op_508_end_0"), val = tensor([1, 282, 513])]; + tensor var_508_end_mask_0 = const()[name = tensor("op_508_end_mask_0"), val = tensor([true, true, false])]; + tensor var_508_cast_fp16 = slice_by_index(begin = var_508_begin_0, end = var_508_end_0, end_mask = var_508_end_mask_0, x = linear_18_cast_fp16)[name = tensor("op_508_cast_fp16")]; + tensor const_5_to_fp16 = const()[name = tensor("const_5_to_fp16"), val = tensor(-inf)]; + tensor var_492_to_fp16 = const()[name = tensor("op_492_to_fp16"), val = tensor(0x1.26cp+2)]; + tensor clip_1_cast_fp16 = clip(alpha = const_5_to_fp16, beta = var_492_to_fp16, x = var_508_cast_fp16)[name = tensor("clip_1_cast_fp16")]; + tensor phase_begin_0 = const()[name = tensor("phase_begin_0"), val = tensor([0, 0, 513])]; + tensor phase_end_0 = const()[name = tensor("phase_end_0"), val = tensor([1, 282, 1026])]; + tensor phase_end_mask_0 = const()[name = tensor("phase_end_mask_0"), val = tensor([true, true, true])]; + tensor phase_cast_fp16 = slice_by_index(begin = phase_begin_0, end = phase_end_0, end_mask = phase_end_mask_0, x = linear_18_cast_fp16)[name = tensor("phase_cast_fp16")]; + tensor mag_cast_fp16 = exp(x = clip_1_cast_fp16)[name = tensor("mag_cast_fp16")]; + tensor var_514_cast_fp16 = cos(x = phase_cast_fp16)[name = tensor("op_514_cast_fp16")]; + tensor real_cast_fp16 = mul(x = mag_cast_fp16, y = var_514_cast_fp16)[name = tensor("real_cast_fp16")]; + tensor var_516_cast_fp16 = sin(x = phase_cast_fp16)[name = tensor("op_516_cast_fp16")]; + tensor imag_cast_fp16 = mul(x = mag_cast_fp16, y = var_516_cast_fp16)[name = tensor("imag_cast_fp16")]; + tensor var_518_cast_fp16 = linear(bias = var_448_bias_0_to_fp16, weight = transpose_0_to_fp16, x = real_cast_fp16)[name = tensor("op_518_cast_fp16")]; + tensor var_519_cast_fp16 = linear(bias = var_448_bias_0_to_fp16, weight = transpose_1_to_fp16, x = imag_cast_fp16)[name = tensor("op_519_cast_fp16")]; + tensor frames_cast_fp16 = add(x = var_518_cast_fp16, y = var_519_cast_fp16)[name = tensor("frames_cast_fp16")]; + tensor var_523_begin_0 = const()[name = tensor("op_523_begin_0"), val = tensor([0, 0, 0])]; + tensor var_523_end_0 = const()[name = tensor("op_523_end_0"), val = tensor([1, 282, 256])]; + tensor var_523_end_mask_0 = const()[name = tensor("op_523_end_mask_0"), val = tensor([true, true, false])]; + tensor var_523_cast_fp16 = slice_by_index(begin = var_523_begin_0, end = var_523_end_0, end_mask = var_523_end_mask_0, x = frames_cast_fp16)[name = tensor("op_523_cast_fp16")]; + tensor var_524 = const()[name = tensor("op_524"), val = tensor([1, 72192])]; + tensor input_121_cast_fp16 = reshape(shape = var_524, x = var_523_cast_fp16)[name = tensor("input_121_cast_fp16")]; + tensor y_11_pad_0 = const()[name = tensor("y_11_pad_0"), val = tensor([0, 0, 0, 768])]; + tensor y_11_mode_0 = const()[name = tensor("y_11_mode_0"), val = tensor("constant")]; + tensor const_6_to_fp16 = const()[name = tensor("const_6_to_fp16"), val = tensor(0x0p+0)]; + tensor y_11_cast_fp16 = pad(constant_val = const_6_to_fp16, mode = y_11_mode_0, pad = y_11_pad_0, x = input_121_cast_fp16)[name = tensor("y_11_cast_fp16")]; + tensor var_530_begin_0 = const()[name = tensor("op_530_begin_0"), val = tensor([0, 0, 256])]; + tensor var_530_end_0 = const()[name = tensor("op_530_end_0"), val = tensor([1, 282, 512])]; + tensor var_530_end_mask_0 = const()[name = tensor("op_530_end_mask_0"), val = tensor([true, true, false])]; + tensor var_530_cast_fp16 = slice_by_index(begin = var_530_begin_0, end = var_530_end_0, end_mask = var_530_end_mask_0, x = frames_cast_fp16)[name = tensor("op_530_cast_fp16")]; + tensor var_531 = const()[name = tensor("op_531"), val = tensor([1, 72192])]; + tensor input_123_cast_fp16 = reshape(shape = var_531, x = var_530_cast_fp16)[name = tensor("input_123_cast_fp16")]; + tensor part_7_pad_0 = const()[name = tensor("part_7_pad_0"), val = tensor([0, 0, 256, 512])]; + tensor part_7_mode_0 = const()[name = tensor("part_7_mode_0"), val = tensor("constant")]; + tensor const_7_to_fp16 = const()[name = tensor("const_7_to_fp16"), val = tensor(0x0p+0)]; + tensor part_7_cast_fp16 = pad(constant_val = const_7_to_fp16, mode = part_7_mode_0, pad = part_7_pad_0, x = input_123_cast_fp16)[name = tensor("part_7_cast_fp16")]; + tensor y_13_cast_fp16 = add(x = y_11_cast_fp16, y = part_7_cast_fp16)[name = tensor("y_13_cast_fp16")]; + tensor var_538_begin_0 = const()[name = tensor("op_538_begin_0"), val = tensor([0, 0, 512])]; + tensor var_538_end_0 = const()[name = tensor("op_538_end_0"), val = tensor([1, 282, 768])]; + tensor var_538_end_mask_0 = const()[name = tensor("op_538_end_mask_0"), val = tensor([true, true, false])]; + tensor var_538_cast_fp16 = slice_by_index(begin = var_538_begin_0, end = var_538_end_0, end_mask = var_538_end_mask_0, x = frames_cast_fp16)[name = tensor("op_538_cast_fp16")]; + tensor var_539 = const()[name = tensor("op_539"), val = tensor([1, 72192])]; + tensor input_125_cast_fp16 = reshape(shape = var_539, x = var_538_cast_fp16)[name = tensor("input_125_cast_fp16")]; + tensor part_9_pad_0 = const()[name = tensor("part_9_pad_0"), val = tensor([0, 0, 512, 256])]; + tensor part_9_mode_0 = const()[name = tensor("part_9_mode_0"), val = tensor("constant")]; + tensor const_8_to_fp16 = const()[name = tensor("const_8_to_fp16"), val = tensor(0x0p+0)]; + tensor part_9_cast_fp16 = pad(constant_val = const_8_to_fp16, mode = part_9_mode_0, pad = part_9_pad_0, x = input_125_cast_fp16)[name = tensor("part_9_cast_fp16")]; + tensor y_15_cast_fp16 = add(x = y_13_cast_fp16, y = part_9_cast_fp16)[name = tensor("y_15_cast_fp16")]; + tensor var_546_begin_0 = const()[name = tensor("op_546_begin_0"), val = tensor([0, 0, 768])]; + tensor var_546_end_0 = const()[name = tensor("op_546_end_0"), val = tensor([1, 282, 1])]; + tensor var_546_end_mask_0 = const()[name = tensor("op_546_end_mask_0"), val = tensor([true, true, true])]; + tensor var_546_cast_fp16 = slice_by_index(begin = var_546_begin_0, end = var_546_end_0, end_mask = var_546_end_mask_0, x = frames_cast_fp16)[name = tensor("op_546_cast_fp16")]; + tensor var_547 = const()[name = tensor("op_547"), val = tensor([1, 72192])]; + tensor input_127_cast_fp16 = reshape(shape = var_547, x = var_546_cast_fp16)[name = tensor("input_127_cast_fp16")]; + tensor part_pad_0 = const()[name = tensor("part_pad_0"), val = tensor([0, 0, 768, 0])]; + tensor part_mode_0 = const()[name = tensor("part_mode_0"), val = tensor("constant")]; + tensor const_9_to_fp16 = const()[name = tensor("const_9_to_fp16"), val = tensor(0x0p+0)]; + tensor part_cast_fp16 = pad(constant_val = const_9_to_fp16, mode = part_mode_0, pad = part_pad_0, x = input_127_cast_fp16)[name = tensor("part_cast_fp16")]; + tensor y_17_cast_fp16 = add(x = y_15_cast_fp16, y = part_cast_fp16)[name = tensor("y_17_cast_fp16")]; + tensor y_19_begin_0 = const()[name = tensor("y_19_begin_0"), val = tensor([0, 512])]; + tensor y_19_end_0 = const()[name = tensor("y_19_end_0"), val = tensor([1, 72448])]; + tensor y_19_end_mask_0 = const()[name = tensor("y_19_end_mask_0"), val = tensor([true, false])]; + tensor y_19_cast_fp16 = slice_by_index(begin = y_19_begin_0, end = y_19_end_0, end_mask = y_19_end_mask_0, x = y_17_cast_fp16)[name = tensor("y_19_cast_fp16")]; + tensor head_24k_env_inv_to_fp16 = const()[name = tensor("head_24k_env_inv_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34262848)))]; + tensor wav_cast_fp16 = mul(x = y_19_cast_fp16, y = head_24k_env_inv_to_fp16)[name = tensor("wav_cast_fp16")]; + tensor input_axes_0 = const()[name = tensor("input_axes_0"), val = tensor([1])]; + tensor input_cast_fp16 = expand_dims(axes = input_axes_0, x = wav_cast_fp16)[name = tensor("input_cast_fp16")]; + tensor x_pad_0 = const()[name = tensor("x_pad_0"), val = tensor([0, 0, 0, 0, 7, 8])]; + tensor x_mode_0 = const()[name = tensor("x_mode_0"), val = tensor("constant")]; + tensor const_10_to_fp16 = const()[name = tensor("const_10_to_fp16"), val = tensor(0x0p+0)]; + tensor x_cast_fp16 = pad(constant_val = const_10_to_fp16, mode = x_mode_0, pad = x_pad_0, x = input_cast_fp16)[name = tensor("x_cast_fp16")]; + tensor y_21_pad_type_0 = const()[name = tensor("y_21_pad_type_0"), val = tensor("valid")]; + tensor y_21_strides_0 = const()[name = tensor("y_21_strides_0"), val = tensor([1])]; + tensor y_21_pad_0 = const()[name = tensor("y_21_pad_0"), val = tensor([0, 0])]; + tensor y_21_dilations_0 = const()[name = tensor("y_21_dilations_0"), val = tensor([1])]; + tensor y_21_groups_0 = const()[name = tensor("y_21_groups_0"), val = tensor(1)]; + tensor resample_kernel_to_fp16 = const()[name = tensor("resample_kernel_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34406784)))]; + tensor y_21_cast_fp16 = conv(dilations = y_21_dilations_0, groups = y_21_groups_0, pad = y_21_pad_0, pad_type = y_21_pad_type_0, strides = y_21_strides_0, weight = resample_kernel_to_fp16, x = x_cast_fp16)[name = tensor("y_21_cast_fp16")]; + tensor var_576_perm_0 = const()[name = tensor("op_576_perm_0"), val = tensor([0, 2, 1])]; + tensor var_577 = const()[name = tensor("op_577"), val = tensor([1, -1])]; + tensor var_576_cast_fp16 = transpose(perm = var_576_perm_0, x = y_21_cast_fp16)[name = tensor("transpose_12")]; + tensor y_cast_fp16 = reshape(shape = var_577, x = var_576_cast_fp16)[name = tensor("y_cast_fp16")]; + tensor low_begin_0 = const()[name = tensor("low_begin_0"), val = tensor([0, 0])]; + tensor low_end_0 = const()[name = tensor("low_end_0"), val = tensor([1, 143872])]; + tensor low_end_mask_0 = const()[name = tensor("low_end_mask_0"), val = tensor([true, false])]; + tensor low_cast_fp16 = slice_by_index(begin = low_begin_0, end = low_end_0, end_mask = low_end_mask_0, x = y_cast_fp16)[name = tensor("low_cast_fp16")]; + tensor high_begin_0 = const()[name = tensor("high_begin_0"), val = tensor([0, 0])]; + tensor high_end_0 = const()[name = tensor("high_end_0"), val = tensor([1, 143872])]; + tensor high_end_mask_0 = const()[name = tensor("high_end_mask_0"), val = tensor([true, false])]; + tensor high_cast_fp16 = slice_by_index(begin = high_begin_0, end = high_end_0, end_mask = high_end_mask_0, x = audio_hi_cast_fp16)[name = tensor("high_cast_fp16")]; + tensor var_598_cast_fp16 = sub(x = high_cast_fp16, y = low_cast_fp16)[name = tensor("op_598_cast_fp16")]; + tensor d_axes_0 = const()[name = tensor("d_axes_0"), val = tensor([1])]; + tensor d_cast_fp16 = expand_dims(axes = d_axes_0, x = var_598_cast_fp16)[name = tensor("d_cast_fp16")]; + tensor hp_pad_type_0 = const()[name = tensor("hp_pad_type_0"), val = tensor("custom")]; + tensor hp_pad_0 = const()[name = tensor("hp_pad_0"), val = tensor([255, 255])]; + tensor hp_strides_0 = const()[name = tensor("hp_strides_0"), val = tensor([1])]; + tensor hp_dilations_0 = const()[name = tensor("hp_dilations_0"), val = tensor([1])]; + tensor hp_groups_0 = const()[name = tensor("hp_groups_0"), val = tensor(1)]; + tensor crossover_weight_to_fp16 = const()[name = tensor("crossover_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34406912)))]; + tensor hp_cast_fp16 = conv(dilations = hp_dilations_0, groups = hp_groups_0, pad = hp_pad_0, pad_type = hp_pad_type_0, strides = hp_strides_0, weight = crossover_weight_to_fp16, x = d_cast_fp16)[name = tensor("hp_cast_fp16")]; + tensor var_605_axes_0 = const()[name = tensor("op_605_axes_0"), val = tensor([1])]; + tensor var_605_cast_fp16 = squeeze(axes = var_605_axes_0, x = hp_cast_fp16)[name = tensor("op_605_cast_fp16")]; + tensor var_606_cast_fp16 = add(x = low_cast_fp16, y = var_605_cast_fp16)[name = tensor("op_606_cast_fp16")]; + tensor var_606_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("op_606_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor audio = cast(dtype = var_606_cast_fp16_to_fp32_dtype_0, x = var_606_cast_fp16)[name = tensor("cast_9")]; + } -> (audio); +} \ No newline at end of file