diff --git "a/original/compiled/UnetChunk2.mlmodelc/model.mil" "b/original/compiled/UnetChunk2.mlmodelc/model.mil" new file mode 100644--- /dev/null +++ "b/original/compiled/UnetChunk2.mlmodelc/model.mil" @@ -0,0 +1,2567 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3304.5.2"}, {"coremlc-version", "3304.6.2"}, {"coremltools-component-milinternal", ""}, {"coremltools-version", "7.1"}})] +{ + func main(tensor encoder_hidden_states, tensor hidden_states_149_cast_fp16, tensor input_115_cast_fp16, tensor input_117_cast_fp16, tensor input_143_cast_fp16, tensor input_15_cast_fp16, tensor input_169_cast_fp16, tensor input_171_cast_fp16, tensor input_253_cast_fp16, tensor input_35_cast_fp16, tensor input_61_cast_fp16, tensor input_63_cast_fp16, tensor input_7_cast_fp16, tensor input_89_cast_fp16) { + tensor cast_0_dtype_0 = const()[name = tensor("cast_0_dtype_0"), val = tensor("fp16")]; + tensor add_1_mean_0_to_fp16 = const()[name = tensor("add_1_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor add_1_variance_0_to_fp16 = const()[name = tensor("add_1_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(768)))]; + tensor cast_10_dtype_0 = const()[name = tensor("cast_10_dtype_0"), val = tensor("fp16")]; + tensor cast_5_dtype_0 = const()[name = tensor("cast_5_dtype_0"), val = tensor("fp16")]; + tensor cast_9_dtype_0 = const()[name = tensor("cast_9_dtype_0"), val = tensor("fp16")]; + tensor cast_8_dtype_0 = const()[name = tensor("cast_8_dtype_0"), val = tensor("fp16")]; + tensor add_15_mean_0_to_fp16 = const()[name = tensor("add_15_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1472)))]; + tensor add_15_variance_0_to_fp16 = const()[name = tensor("add_15_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2816)))]; + tensor cast_7_dtype_0 = const()[name = tensor("cast_7_dtype_0"), val = tensor("fp16")]; + tensor cast_12_dtype_0 = const()[name = tensor("cast_12_dtype_0"), val = tensor("fp16")]; + tensor cast_11_dtype_0 = const()[name = tensor("cast_11_dtype_0"), val = tensor("fp16")]; + tensor add_27_mean_0_to_fp16 = const()[name = tensor("add_27_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4160)))]; + tensor add_27_variance_0_to_fp16 = const()[name = tensor("add_27_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6784)))]; + tensor cast_2_dtype_0 = const()[name = tensor("cast_2_dtype_0"), val = tensor("fp16")]; + tensor cast_3_dtype_0 = const()[name = tensor("cast_3_dtype_0"), val = tensor("fp16")]; + tensor cast_4_dtype_0 = const()[name = tensor("cast_4_dtype_0"), val = tensor("fp16")]; + tensor var_2128 = const()[name = tensor("op_2128"), val = tensor(1)]; + tensor add_55_mean_0_to_fp16 = const()[name = tensor("add_55_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9408)))]; + tensor add_55_variance_0_to_fp16 = const()[name = tensor("add_55_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14592)))]; + tensor cast_1_dtype_0 = const()[name = tensor("cast_1_dtype_0"), val = tensor("fp16")]; + tensor cast_6_dtype_0 = const()[name = tensor("cast_6_dtype_0"), val = tensor("fp16")]; + tensor var_2225 = const()[name = tensor("op_2225"), val = tensor([1, 1])]; + tensor var_2227 = const()[name = tensor("op_2227"), val = tensor([1, 1])]; + tensor x_7_pad_type_0 = const()[name = tensor("x_7_pad_type_0"), val = tensor("custom")]; + tensor x_7_pad_0 = const()[name = tensor("x_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19776)))]; + tensor up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6573440)))]; + tensor cast_2 = cast(dtype = cast_1_dtype_0, x = input_253_cast_fp16)[name = tensor("cast_2")]; + tensor x_7_cast_fp16 = conv(bias = up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_2227, groups = var_2128, pad = x_7_pad_0, pad_type = x_7_pad_type_0, strides = var_2225, weight = up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16, x = cast_2)[name = tensor("x_7_cast_fp16")]; + tensor cast_1 = cast(dtype = cast_6_dtype_0, x = hidden_states_149_cast_fp16)[name = tensor("cast_1")]; + tensor hidden_states_151_cast_fp16 = add(x = x_7_cast_fp16, y = cast_1)[name = tensor("hidden_states_151_cast_fp16")]; + tensor input_267_interleave_0 = const()[name = tensor("input_267_interleave_0"), val = tensor(false)]; + tensor cast_3 = cast(dtype = cast_4_dtype_0, x = input_171_cast_fp16)[name = tensor("cast_3")]; + tensor input_267_cast_fp16 = concat(axis = var_2128, interleave = input_267_interleave_0, values = (hidden_states_151_cast_fp16, cast_3))[name = tensor("input_267_cast_fp16")]; + tensor reshape_124_shape_0 = const()[name = tensor("reshape_124_shape_0"), val = tensor([2, 32, 80, 6, 10])]; + tensor reshape_124_cast_fp16 = reshape(shape = reshape_124_shape_0, x = input_267_cast_fp16)[name = tensor("reshape_124_cast_fp16")]; + tensor reduce_mean_93_axes_0 = const()[name = tensor("reduce_mean_93_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_93_keep_dims_0 = const()[name = tensor("reduce_mean_93_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_93_cast_fp16 = reduce_mean(axes = reduce_mean_93_axes_0, keep_dims = reduce_mean_93_keep_dims_0, x = reshape_124_cast_fp16)[name = tensor("reduce_mean_93_cast_fp16")]; + tensor sub_62_cast_fp16 = sub(x = reshape_124_cast_fp16, y = reduce_mean_93_cast_fp16)[name = tensor("sub_62_cast_fp16")]; + tensor square_31_cast_fp16 = square(x = sub_62_cast_fp16)[name = tensor("square_31_cast_fp16")]; + tensor reduce_mean_95_axes_0 = const()[name = tensor("reduce_mean_95_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_95_keep_dims_0 = const()[name = tensor("reduce_mean_95_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_95_cast_fp16 = reduce_mean(axes = reduce_mean_95_axes_0, keep_dims = reduce_mean_95_keep_dims_0, x = square_31_cast_fp16)[name = tensor("reduce_mean_95_cast_fp16")]; + tensor add_62_y_0_to_fp16 = const()[name = tensor("add_62_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_62_cast_fp16 = add(x = reduce_mean_95_cast_fp16, y = add_62_y_0_to_fp16)[name = tensor("add_62_cast_fp16")]; + tensor sqrt_31_cast_fp16 = sqrt(x = add_62_cast_fp16)[name = tensor("sqrt_31_cast_fp16")]; + tensor real_div_31_cast_fp16 = real_div(x = sub_62_cast_fp16, y = sqrt_31_cast_fp16)[name = tensor("real_div_31_cast_fp16")]; + tensor reshape_125_shape_0 = const()[name = tensor("reshape_125_shape_0"), val = tensor([2, 2560, 6, 10])]; + tensor reshape_125_cast_fp16 = reshape(shape = reshape_125_shape_0, x = real_div_31_cast_fp16)[name = tensor("reshape_125_cast_fp16")]; + tensor add_63_gamma_0_to_fp16 = const()[name = tensor("add_63_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6576064)))]; + tensor add_63_beta_0_to_fp16 = const()[name = tensor("add_63_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6581248)))]; + tensor add_63_epsilon_0_to_fp16 = const()[name = tensor("add_63_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_63_cast_fp16 = batch_norm(beta = add_63_beta_0_to_fp16, epsilon = add_63_epsilon_0_to_fp16, gamma = add_63_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_125_cast_fp16)[name = tensor("add_63_cast_fp16")]; + tensor input_271_cast_fp16 = silu(x = add_63_cast_fp16)[name = tensor("input_271_cast_fp16")]; + tensor var_2245 = const()[name = tensor("op_2245"), val = tensor([1, 1])]; + tensor var_2247 = const()[name = tensor("op_2247"), val = tensor([1, 1])]; + tensor hidden_states_153_pad_type_0 = const()[name = tensor("hidden_states_153_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_153_pad_0 = const()[name = tensor("hidden_states_153_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6586432)))]; + tensor up_blocks_0_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65568896)))]; + tensor hidden_states_153_cast_fp16 = conv(bias = up_blocks_0_resnets_2_conv1_bias_to_fp16, dilations = var_2247, groups = var_2128, pad = hidden_states_153_pad_0, pad_type = hidden_states_153_pad_type_0, strides = var_2245, weight = up_blocks_0_resnets_2_conv1_weight_to_fp16, x = input_271_cast_fp16)[name = tensor("hidden_states_153_cast_fp16")]; + tensor var_2253 = const()[name = tensor("op_2253"), val = tensor([1, 1])]; + tensor var_2255 = const()[name = tensor("op_2255"), val = tensor([1, 1])]; + tensor temb_25_pad_type_0 = const()[name = tensor("temb_25_pad_type_0"), val = tensor("custom")]; + tensor temb_25_pad_0 = const()[name = tensor("temb_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65571520)))]; + tensor up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68848384)))]; + tensor cast_12 = cast(dtype = cast_10_dtype_0, x = input_15_cast_fp16)[name = tensor("cast_12")]; + tensor temb_25_cast_fp16 = conv(bias = up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_2255, groups = var_2128, pad = temb_25_pad_0, pad_type = temb_25_pad_type_0, strides = var_2253, weight = up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16, x = cast_12)[name = tensor("temb_25_cast_fp16")]; + tensor input_275_cast_fp16 = add(x = hidden_states_153_cast_fp16, y = temb_25_cast_fp16)[name = tensor("input_275_cast_fp16")]; + tensor reshape_128_shape_0 = const()[name = tensor("reshape_128_shape_0"), val = tensor([2, 32, 40, 6, 10])]; + tensor reshape_128_cast_fp16 = reshape(shape = reshape_128_shape_0, x = input_275_cast_fp16)[name = tensor("reshape_128_cast_fp16")]; + tensor reduce_mean_96_axes_0 = const()[name = tensor("reduce_mean_96_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_96_keep_dims_0 = const()[name = tensor("reduce_mean_96_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_96_cast_fp16 = reduce_mean(axes = reduce_mean_96_axes_0, keep_dims = reduce_mean_96_keep_dims_0, x = reshape_128_cast_fp16)[name = tensor("reduce_mean_96_cast_fp16")]; + tensor sub_64_cast_fp16 = sub(x = reshape_128_cast_fp16, y = reduce_mean_96_cast_fp16)[name = tensor("sub_64_cast_fp16")]; + tensor square_32_cast_fp16 = square(x = sub_64_cast_fp16)[name = tensor("square_32_cast_fp16")]; + tensor reduce_mean_98_axes_0 = const()[name = tensor("reduce_mean_98_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_98_keep_dims_0 = const()[name = tensor("reduce_mean_98_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_98_cast_fp16 = reduce_mean(axes = reduce_mean_98_axes_0, keep_dims = reduce_mean_98_keep_dims_0, x = square_32_cast_fp16)[name = tensor("reduce_mean_98_cast_fp16")]; + tensor add_64_y_0_to_fp16 = const()[name = tensor("add_64_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_64_cast_fp16 = add(x = reduce_mean_98_cast_fp16, y = add_64_y_0_to_fp16)[name = tensor("add_64_cast_fp16")]; + tensor sqrt_32_cast_fp16 = sqrt(x = add_64_cast_fp16)[name = tensor("sqrt_32_cast_fp16")]; + tensor real_div_32_cast_fp16 = real_div(x = sub_64_cast_fp16, y = sqrt_32_cast_fp16)[name = tensor("real_div_32_cast_fp16")]; + tensor reshape_129_shape_0 = const()[name = tensor("reshape_129_shape_0"), val = tensor([2, 1280, 6, 10])]; + tensor reshape_129_cast_fp16 = reshape(shape = reshape_129_shape_0, x = real_div_32_cast_fp16)[name = tensor("reshape_129_cast_fp16")]; + tensor add_65_gamma_0_to_fp16 = const()[name = tensor("add_65_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68851008)))]; + tensor add_65_beta_0_to_fp16 = const()[name = tensor("add_65_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68853632)))]; + tensor add_65_epsilon_0_to_fp16 = const()[name = tensor("add_65_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_65_cast_fp16 = batch_norm(beta = add_65_beta_0_to_fp16, epsilon = add_65_epsilon_0_to_fp16, gamma = add_65_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_129_cast_fp16)[name = tensor("add_65_cast_fp16")]; + tensor input_279_cast_fp16 = silu(x = add_65_cast_fp16)[name = tensor("input_279_cast_fp16")]; + tensor var_2265 = const()[name = tensor("op_2265"), val = tensor([1, 1])]; + tensor var_2267 = const()[name = tensor("op_2267"), val = tensor([1, 1])]; + tensor hidden_states_155_pad_type_0 = const()[name = tensor("hidden_states_155_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_155_pad_0 = const()[name = tensor("hidden_states_155_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68856256)))]; + tensor up_blocks_0_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98347520)))]; + tensor hidden_states_155_cast_fp16 = conv(bias = up_blocks_0_resnets_2_conv2_bias_to_fp16, dilations = var_2267, groups = var_2128, pad = hidden_states_155_pad_0, pad_type = hidden_states_155_pad_type_0, strides = var_2265, weight = up_blocks_0_resnets_2_conv2_weight_to_fp16, x = input_279_cast_fp16)[name = tensor("hidden_states_155_cast_fp16")]; + tensor var_2272 = const()[name = tensor("op_2272"), val = tensor([1, 1])]; + tensor var_2274 = const()[name = tensor("op_2274"), val = tensor([1, 1])]; + tensor x_9_pad_type_0 = const()[name = tensor("x_9_pad_type_0"), val = tensor("custom")]; + tensor x_9_pad_0 = const()[name = tensor("x_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98350144)))]; + tensor up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104903808)))]; + tensor x_9_cast_fp16 = conv(bias = up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_2274, groups = var_2128, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = var_2272, weight = up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16, x = input_267_cast_fp16)[name = tensor("x_9_cast_fp16")]; + tensor input_281_cast_fp16 = add(x = x_9_cast_fp16, y = hidden_states_155_cast_fp16)[name = tensor("input_281_cast_fp16")]; + tensor input_283_scale_factor_height_0 = const()[name = tensor("input_283_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_283_scale_factor_width_0 = const()[name = tensor("input_283_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_283_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_283_scale_factor_height_0, scale_factor_width = input_283_scale_factor_width_0, x = input_281_cast_fp16)[name = tensor("input_283_cast_fp16")]; + tensor var_2283 = const()[name = tensor("op_2283"), val = tensor([1, 1])]; + tensor var_2285 = const()[name = tensor("op_2285"), val = tensor([1, 1])]; + tensor hidden_states_157_pad_type_0 = const()[name = tensor("hidden_states_157_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_157_pad_0 = const()[name = tensor("hidden_states_157_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("up_blocks_0_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104906432)))]; + tensor up_blocks_0_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_0_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134397696)))]; + tensor hidden_states_157_cast_fp16 = conv(bias = up_blocks_0_upsamplers_0_conv_bias_to_fp16, dilations = var_2285, groups = var_2128, pad = hidden_states_157_pad_0, pad_type = hidden_states_157_pad_type_0, strides = var_2283, weight = up_blocks_0_upsamplers_0_conv_weight_to_fp16, x = input_283_cast_fp16)[name = tensor("hidden_states_157_cast_fp16")]; + tensor var_2290 = const()[name = tensor("op_2290"), val = tensor(3)]; + tensor var_2301 = const()[name = tensor("op_2301"), val = tensor(true)]; + tensor var_2306 = const()[name = tensor("op_2306"), val = tensor(1)]; + tensor input_285_interleave_0 = const()[name = tensor("input_285_interleave_0"), val = tensor(false)]; + tensor cast_4 = cast(dtype = cast_3_dtype_0, x = input_169_cast_fp16)[name = tensor("cast_4")]; + tensor input_285_cast_fp16 = concat(axis = var_2306, interleave = input_285_interleave_0, values = (hidden_states_157_cast_fp16, cast_4))[name = tensor("input_285_cast_fp16")]; + tensor reshape_132_shape_0 = const()[name = tensor("reshape_132_shape_0"), val = tensor([2, 32, 80, 12, 20])]; + tensor reshape_132_cast_fp16 = reshape(shape = reshape_132_shape_0, x = input_285_cast_fp16)[name = tensor("reshape_132_cast_fp16")]; + tensor reduce_mean_99_axes_0 = const()[name = tensor("reduce_mean_99_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_99_keep_dims_0 = const()[name = tensor("reduce_mean_99_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_99_cast_fp16 = reduce_mean(axes = reduce_mean_99_axes_0, keep_dims = reduce_mean_99_keep_dims_0, x = reshape_132_cast_fp16)[name = tensor("reduce_mean_99_cast_fp16")]; + tensor sub_66_cast_fp16 = sub(x = reshape_132_cast_fp16, y = reduce_mean_99_cast_fp16)[name = tensor("sub_66_cast_fp16")]; + tensor square_33_cast_fp16 = square(x = sub_66_cast_fp16)[name = tensor("square_33_cast_fp16")]; + tensor reduce_mean_101_axes_0 = const()[name = tensor("reduce_mean_101_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_101_keep_dims_0 = const()[name = tensor("reduce_mean_101_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_101_cast_fp16 = reduce_mean(axes = reduce_mean_101_axes_0, keep_dims = reduce_mean_101_keep_dims_0, x = square_33_cast_fp16)[name = tensor("reduce_mean_101_cast_fp16")]; + tensor add_66_y_0_to_fp16 = const()[name = tensor("add_66_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_66_cast_fp16 = add(x = reduce_mean_101_cast_fp16, y = add_66_y_0_to_fp16)[name = tensor("add_66_cast_fp16")]; + tensor sqrt_33_cast_fp16 = sqrt(x = add_66_cast_fp16)[name = tensor("sqrt_33_cast_fp16")]; + tensor real_div_33_cast_fp16 = real_div(x = sub_66_cast_fp16, y = sqrt_33_cast_fp16)[name = tensor("real_div_33_cast_fp16")]; + tensor reshape_133_shape_0 = const()[name = tensor("reshape_133_shape_0"), val = tensor([2, 2560, 12, 20])]; + tensor reshape_133_cast_fp16 = reshape(shape = reshape_133_shape_0, x = real_div_33_cast_fp16)[name = tensor("reshape_133_cast_fp16")]; + tensor add_67_gamma_0_to_fp16 = const()[name = tensor("add_67_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134400320)))]; + tensor add_67_beta_0_to_fp16 = const()[name = tensor("add_67_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134405504)))]; + tensor add_67_epsilon_0_to_fp16 = const()[name = tensor("add_67_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_67_cast_fp16 = batch_norm(beta = add_67_beta_0_to_fp16, epsilon = add_67_epsilon_0_to_fp16, gamma = add_67_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_133_cast_fp16)[name = tensor("add_67_cast_fp16")]; + tensor input_289_cast_fp16 = silu(x = add_67_cast_fp16)[name = tensor("input_289_cast_fp16")]; + tensor var_2335 = const()[name = tensor("op_2335"), val = tensor([1, 1])]; + tensor var_2337 = const()[name = tensor("op_2337"), val = tensor([1, 1])]; + tensor hidden_states_159_pad_type_0 = const()[name = tensor("hidden_states_159_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_159_pad_0 = const()[name = tensor("hidden_states_159_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134410688)))]; + tensor up_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193393152)))]; + tensor hidden_states_159_cast_fp16 = conv(bias = up_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_2337, groups = var_2306, pad = hidden_states_159_pad_0, pad_type = hidden_states_159_pad_type_0, strides = var_2335, weight = up_blocks_1_resnets_0_conv1_weight_to_fp16, x = input_289_cast_fp16)[name = tensor("hidden_states_159_cast_fp16")]; + tensor var_2343 = const()[name = tensor("op_2343"), val = tensor([1, 1])]; + tensor var_2345 = const()[name = tensor("op_2345"), val = tensor([1, 1])]; + tensor temb_27_pad_type_0 = const()[name = tensor("temb_27_pad_type_0"), val = tensor("custom")]; + tensor temb_27_pad_0 = const()[name = tensor("temb_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193395776)))]; + tensor up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196672640)))]; + tensor temb_27_cast_fp16 = conv(bias = up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_2345, groups = var_2306, pad = temb_27_pad_0, pad_type = temb_27_pad_type_0, strides = var_2343, weight = up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16, x = cast_12)[name = tensor("temb_27_cast_fp16")]; + tensor input_293_cast_fp16 = add(x = hidden_states_159_cast_fp16, y = temb_27_cast_fp16)[name = tensor("input_293_cast_fp16")]; + tensor reshape_136_shape_0 = const()[name = tensor("reshape_136_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_136_cast_fp16 = reshape(shape = reshape_136_shape_0, x = input_293_cast_fp16)[name = tensor("reshape_136_cast_fp16")]; + tensor reduce_mean_102_axes_0 = const()[name = tensor("reduce_mean_102_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_102_keep_dims_0 = const()[name = tensor("reduce_mean_102_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_102_cast_fp16 = reduce_mean(axes = reduce_mean_102_axes_0, keep_dims = reduce_mean_102_keep_dims_0, x = reshape_136_cast_fp16)[name = tensor("reduce_mean_102_cast_fp16")]; + tensor sub_68_cast_fp16 = sub(x = reshape_136_cast_fp16, y = reduce_mean_102_cast_fp16)[name = tensor("sub_68_cast_fp16")]; + tensor square_34_cast_fp16 = square(x = sub_68_cast_fp16)[name = tensor("square_34_cast_fp16")]; + tensor reduce_mean_104_axes_0 = const()[name = tensor("reduce_mean_104_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_104_keep_dims_0 = const()[name = tensor("reduce_mean_104_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_104_cast_fp16 = reduce_mean(axes = reduce_mean_104_axes_0, keep_dims = reduce_mean_104_keep_dims_0, x = square_34_cast_fp16)[name = tensor("reduce_mean_104_cast_fp16")]; + tensor add_68_y_0_to_fp16 = const()[name = tensor("add_68_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_68_cast_fp16 = add(x = reduce_mean_104_cast_fp16, y = add_68_y_0_to_fp16)[name = tensor("add_68_cast_fp16")]; + tensor sqrt_34_cast_fp16 = sqrt(x = add_68_cast_fp16)[name = tensor("sqrt_34_cast_fp16")]; + tensor real_div_34_cast_fp16 = real_div(x = sub_68_cast_fp16, y = sqrt_34_cast_fp16)[name = tensor("real_div_34_cast_fp16")]; + tensor reshape_137_shape_0 = const()[name = tensor("reshape_137_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_137_cast_fp16 = reshape(shape = reshape_137_shape_0, x = real_div_34_cast_fp16)[name = tensor("reshape_137_cast_fp16")]; + tensor add_69_gamma_0_to_fp16 = const()[name = tensor("add_69_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196675264)))]; + tensor add_69_beta_0_to_fp16 = const()[name = tensor("add_69_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196677888)))]; + tensor add_69_epsilon_0_to_fp16 = const()[name = tensor("add_69_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_69_cast_fp16 = batch_norm(beta = add_69_beta_0_to_fp16, epsilon = add_69_epsilon_0_to_fp16, gamma = add_69_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_137_cast_fp16)[name = tensor("add_69_cast_fp16")]; + tensor input_297_cast_fp16 = silu(x = add_69_cast_fp16)[name = tensor("input_297_cast_fp16")]; + tensor var_2355 = const()[name = tensor("op_2355"), val = tensor([1, 1])]; + tensor var_2357 = const()[name = tensor("op_2357"), val = tensor([1, 1])]; + tensor hidden_states_161_pad_type_0 = const()[name = tensor("hidden_states_161_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_161_pad_0 = const()[name = tensor("hidden_states_161_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196680512)))]; + tensor up_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(226171776)))]; + tensor hidden_states_161_cast_fp16 = conv(bias = up_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_2357, groups = var_2306, pad = hidden_states_161_pad_0, pad_type = hidden_states_161_pad_type_0, strides = var_2355, weight = up_blocks_1_resnets_0_conv2_weight_to_fp16, x = input_297_cast_fp16)[name = tensor("hidden_states_161_cast_fp16")]; + tensor var_2362 = const()[name = tensor("op_2362"), val = tensor([1, 1])]; + tensor var_2364 = const()[name = tensor("op_2364"), val = tensor([1, 1])]; + tensor x_11_pad_type_0 = const()[name = tensor("x_11_pad_type_0"), val = tensor("custom")]; + tensor x_11_pad_0 = const()[name = tensor("x_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(226174400)))]; + tensor up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232728064)))]; + tensor x_11_cast_fp16 = conv(bias = up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_2364, groups = var_2306, pad = x_11_pad_0, pad_type = x_11_pad_type_0, strides = var_2362, weight = up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16, x = input_285_cast_fp16)[name = tensor("x_11_cast_fp16")]; + tensor hidden_states_163_cast_fp16 = add(x = x_11_cast_fp16, y = hidden_states_161_cast_fp16)[name = tensor("hidden_states_163_cast_fp16")]; + tensor reshape_140_shape_0 = const()[name = tensor("reshape_140_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_140_cast_fp16 = reshape(shape = reshape_140_shape_0, x = hidden_states_163_cast_fp16)[name = tensor("reshape_140_cast_fp16")]; + tensor reduce_mean_105_axes_0 = const()[name = tensor("reduce_mean_105_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_105_keep_dims_0 = const()[name = tensor("reduce_mean_105_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_105_cast_fp16 = reduce_mean(axes = reduce_mean_105_axes_0, keep_dims = reduce_mean_105_keep_dims_0, x = reshape_140_cast_fp16)[name = tensor("reduce_mean_105_cast_fp16")]; + tensor sub_70_cast_fp16 = sub(x = reshape_140_cast_fp16, y = reduce_mean_105_cast_fp16)[name = tensor("sub_70_cast_fp16")]; + tensor square_35_cast_fp16 = square(x = sub_70_cast_fp16)[name = tensor("square_35_cast_fp16")]; + tensor reduce_mean_107_axes_0 = const()[name = tensor("reduce_mean_107_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_107_keep_dims_0 = const()[name = tensor("reduce_mean_107_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_107_cast_fp16 = reduce_mean(axes = reduce_mean_107_axes_0, keep_dims = reduce_mean_107_keep_dims_0, x = square_35_cast_fp16)[name = tensor("reduce_mean_107_cast_fp16")]; + tensor add_70_y_0_to_fp16 = const()[name = tensor("add_70_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_70_cast_fp16 = add(x = reduce_mean_107_cast_fp16, y = add_70_y_0_to_fp16)[name = tensor("add_70_cast_fp16")]; + tensor sqrt_35_cast_fp16 = sqrt(x = add_70_cast_fp16)[name = tensor("sqrt_35_cast_fp16")]; + tensor real_div_35_cast_fp16 = real_div(x = sub_70_cast_fp16, y = sqrt_35_cast_fp16)[name = tensor("real_div_35_cast_fp16")]; + tensor reshape_141_shape_0 = const()[name = tensor("reshape_141_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_141_cast_fp16 = reshape(shape = reshape_141_shape_0, x = real_div_35_cast_fp16)[name = tensor("reshape_141_cast_fp16")]; + tensor add_71_gamma_0_to_fp16 = const()[name = tensor("add_71_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232730688)))]; + tensor add_71_beta_0_to_fp16 = const()[name = tensor("add_71_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232733312)))]; + tensor add_71_epsilon_0_to_fp16 = const()[name = tensor("add_71_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_71_cast_fp16 = batch_norm(beta = add_71_beta_0_to_fp16, epsilon = add_71_epsilon_0_to_fp16, gamma = add_71_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_141_cast_fp16)[name = tensor("add_71_cast_fp16")]; + tensor var_2384 = const()[name = tensor("op_2384"), val = tensor([1, 1])]; + tensor var_2386 = const()[name = tensor("op_2386"), val = tensor([1, 1])]; + tensor hidden_states_165_pad_type_0 = const()[name = tensor("hidden_states_165_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_165_pad_0 = const()[name = tensor("hidden_states_165_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232735936)))]; + tensor up_blocks_1_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236012800)))]; + tensor hidden_states_165_cast_fp16 = conv(bias = up_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_2386, groups = var_2306, pad = hidden_states_165_pad_0, pad_type = hidden_states_165_pad_type_0, strides = var_2384, weight = up_blocks_1_attentions_0_proj_in_weight_to_fp16, x = add_71_cast_fp16)[name = tensor("hidden_states_165_cast_fp16")]; + tensor var_2391 = const()[name = tensor("op_2391"), val = tensor([2, 1280, 1, 240])]; + tensor inputs_43_cast_fp16 = reshape(shape = var_2391, x = hidden_states_165_cast_fp16)[name = tensor("inputs_43_cast_fp16")]; + tensor var_2401 = const()[name = tensor("op_2401"), val = tensor([1])]; + tensor channels_mean_43_cast_fp16 = reduce_mean(axes = var_2401, keep_dims = var_2301, x = inputs_43_cast_fp16)[name = tensor("channels_mean_43_cast_fp16")]; + tensor zero_mean_43_cast_fp16 = sub(x = inputs_43_cast_fp16, y = channels_mean_43_cast_fp16)[name = tensor("zero_mean_43_cast_fp16")]; + tensor zero_mean_sq_43_cast_fp16 = mul(x = zero_mean_43_cast_fp16, y = zero_mean_43_cast_fp16)[name = tensor("zero_mean_sq_43_cast_fp16")]; + tensor var_2405 = const()[name = tensor("op_2405"), val = tensor([1])]; + tensor var_2406_cast_fp16 = reduce_mean(axes = var_2405, keep_dims = var_2301, x = zero_mean_sq_43_cast_fp16)[name = tensor("op_2406_cast_fp16")]; + tensor var_2407_to_fp16 = const()[name = tensor("op_2407_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2408_cast_fp16 = add(x = var_2406_cast_fp16, y = var_2407_to_fp16)[name = tensor("op_2408_cast_fp16")]; + tensor denom_43_epsilon_0_to_fp16 = const()[name = tensor("denom_43_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_43_cast_fp16 = rsqrt(epsilon = denom_43_epsilon_0_to_fp16, x = var_2408_cast_fp16)[name = tensor("denom_43_cast_fp16")]; + tensor out_43_cast_fp16 = mul(x = zero_mean_43_cast_fp16, y = denom_43_cast_fp16)[name = tensor("out_43_cast_fp16")]; + tensor var_2412_to_fp16 = const()[name = tensor("op_2412_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236015424)))]; + tensor var_2413_cast_fp16 = add(x = out_43_cast_fp16, y = var_2412_to_fp16)[name = tensor("op_2413_cast_fp16")]; + tensor var_2415_to_fp16 = const()[name = tensor("op_2415_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236018048)))]; + tensor hidden_states_167_cast_fp16 = mul(x = var_2413_cast_fp16, y = var_2415_to_fp16)[name = tensor("hidden_states_167_cast_fp16")]; + tensor var_2422 = const()[name = tensor("op_2422"), val = tensor([1, 1])]; + tensor var_2424 = const()[name = tensor("op_2424"), val = tensor([1, 1])]; + tensor q_29_pad_type_0 = const()[name = tensor("q_29_pad_type_0"), val = tensor("custom")]; + tensor q_29_pad_0 = const()[name = tensor("q_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236020672)))]; + tensor q_29_cast_fp16 = conv(dilations = var_2424, groups = var_2306, pad = q_29_pad_0, pad_type = q_29_pad_type_0, strides = var_2422, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_167_cast_fp16)[name = tensor("q_29_cast_fp16")]; + tensor var_2428 = const()[name = tensor("op_2428"), val = tensor([1, 1])]; + tensor var_2430 = const()[name = tensor("op_2430"), val = tensor([1, 1])]; + tensor k_29_pad_type_0 = const()[name = tensor("k_29_pad_type_0"), val = tensor("custom")]; + tensor k_29_pad_0 = const()[name = tensor("k_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239297536)))]; + tensor k_29_cast_fp16 = conv(dilations = var_2430, groups = var_2306, pad = k_29_pad_0, pad_type = k_29_pad_type_0, strides = var_2428, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_167_cast_fp16)[name = tensor("k_29_cast_fp16")]; + tensor var_2434 = const()[name = tensor("op_2434"), val = tensor([1, 1])]; + tensor var_2436 = const()[name = tensor("op_2436"), val = tensor([1, 1])]; + tensor v_29_pad_type_0 = const()[name = tensor("v_29_pad_type_0"), val = tensor("custom")]; + tensor v_29_pad_0 = const()[name = tensor("v_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(242574400)))]; + tensor v_29_cast_fp16 = conv(dilations = var_2436, groups = var_2306, pad = v_29_pad_0, pad_type = v_29_pad_type_0, strides = var_2434, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_167_cast_fp16)[name = tensor("v_29_cast_fp16")]; + tensor var_2440 = const()[name = tensor("op_2440"), val = tensor([2, 20, 64, -1])]; + tensor var_2441_cast_fp16 = reshape(shape = var_2440, x = q_29_cast_fp16)[name = tensor("op_2441_cast_fp16")]; + tensor var_2442 = const()[name = tensor("op_2442"), val = tensor([2, 20, 64, -1])]; + tensor var_2443_cast_fp16 = reshape(shape = var_2442, x = k_29_cast_fp16)[name = tensor("op_2443_cast_fp16")]; + tensor var_2444 = const()[name = tensor("op_2444"), val = tensor([2, 20, 64, -1])]; + tensor var_2445_cast_fp16 = reshape(shape = var_2444, x = v_29_cast_fp16)[name = tensor("op_2445_cast_fp16")]; + tensor attn_weights_57_transpose_x_0 = const()[name = tensor("attn_weights_57_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_57_transpose_y_0 = const()[name = tensor("attn_weights_57_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_57_cast_fp16 = matmul(transpose_x = attn_weights_57_transpose_x_0, transpose_y = attn_weights_57_transpose_y_0, x = var_2441_cast_fp16, y = var_2443_cast_fp16)[name = tensor("attn_weights_57_cast_fp16")]; + tensor var_2297_to_fp16 = const()[name = tensor("op_2297_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_59_cast_fp16 = mul(x = attn_weights_57_cast_fp16, y = var_2297_to_fp16)[name = tensor("attn_weights_59_cast_fp16")]; + tensor var_2449_cast_fp16 = softmax(axis = var_2290, x = attn_weights_59_cast_fp16)[name = tensor("op_2449_cast_fp16")]; + tensor attn_29_transpose_x_0 = const()[name = tensor("attn_29_transpose_x_0"), val = tensor(false)]; + tensor attn_29_transpose_y_0 = const()[name = tensor("attn_29_transpose_y_0"), val = tensor(true)]; + tensor attn_29_cast_fp16 = matmul(transpose_x = attn_29_transpose_x_0, transpose_y = attn_29_transpose_y_0, x = var_2445_cast_fp16, y = var_2449_cast_fp16)[name = tensor("attn_29_cast_fp16")]; + tensor var_2453 = const()[name = tensor("op_2453"), val = tensor([2, 1280, 1, -1])]; + tensor input_301_cast_fp16 = reshape(shape = var_2453, x = attn_29_cast_fp16)[name = tensor("input_301_cast_fp16")]; + tensor var_2458 = const()[name = tensor("op_2458"), val = tensor([1, 1])]; + tensor var_2460 = const()[name = tensor("op_2460"), val = tensor([1, 1])]; + tensor var_2462_pad_type_0 = const()[name = tensor("op_2462_pad_type_0"), val = tensor("custom")]; + tensor var_2462_pad_0 = const()[name = tensor("op_2462_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(245851264)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(249128128)))]; + tensor var_2462_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_2460, groups = var_2306, pad = var_2462_pad_0, pad_type = var_2462_pad_type_0, strides = var_2458, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_301_cast_fp16)[name = tensor("op_2462_cast_fp16")]; + tensor inputs_45_cast_fp16 = add(x = var_2462_cast_fp16, y = inputs_43_cast_fp16)[name = tensor("inputs_45_cast_fp16")]; + tensor var_2466 = const()[name = tensor("op_2466"), val = tensor([1])]; + tensor channels_mean_45_cast_fp16 = reduce_mean(axes = var_2466, keep_dims = var_2301, x = inputs_45_cast_fp16)[name = tensor("channels_mean_45_cast_fp16")]; + tensor zero_mean_45_cast_fp16 = sub(x = inputs_45_cast_fp16, y = channels_mean_45_cast_fp16)[name = tensor("zero_mean_45_cast_fp16")]; + tensor zero_mean_sq_45_cast_fp16 = mul(x = zero_mean_45_cast_fp16, y = zero_mean_45_cast_fp16)[name = tensor("zero_mean_sq_45_cast_fp16")]; + tensor var_2470 = const()[name = tensor("op_2470"), val = tensor([1])]; + tensor var_2471_cast_fp16 = reduce_mean(axes = var_2470, keep_dims = var_2301, x = zero_mean_sq_45_cast_fp16)[name = tensor("op_2471_cast_fp16")]; + tensor var_2472_to_fp16 = const()[name = tensor("op_2472_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2473_cast_fp16 = add(x = var_2471_cast_fp16, y = var_2472_to_fp16)[name = tensor("op_2473_cast_fp16")]; + tensor denom_45_epsilon_0_to_fp16 = const()[name = tensor("denom_45_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_45_cast_fp16 = rsqrt(epsilon = denom_45_epsilon_0_to_fp16, x = var_2473_cast_fp16)[name = tensor("denom_45_cast_fp16")]; + tensor out_45_cast_fp16 = mul(x = zero_mean_45_cast_fp16, y = denom_45_cast_fp16)[name = tensor("out_45_cast_fp16")]; + tensor var_2477_to_fp16 = const()[name = tensor("op_2477_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(249130752)))]; + tensor var_2478_cast_fp16 = add(x = out_45_cast_fp16, y = var_2477_to_fp16)[name = tensor("op_2478_cast_fp16")]; + tensor var_2480_to_fp16 = const()[name = tensor("op_2480_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(249133376)))]; + tensor hidden_states_169_cast_fp16 = mul(x = var_2478_cast_fp16, y = var_2480_to_fp16)[name = tensor("hidden_states_169_cast_fp16")]; + tensor var_2487 = const()[name = tensor("op_2487"), val = tensor([1, 1])]; + tensor var_2489 = const()[name = tensor("op_2489"), val = tensor([1, 1])]; + tensor q_31_pad_type_0 = const()[name = tensor("q_31_pad_type_0"), val = tensor("custom")]; + tensor q_31_pad_0 = const()[name = tensor("q_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(249136000)))]; + tensor q_31_cast_fp16 = conv(dilations = var_2489, groups = var_2306, pad = q_31_pad_0, pad_type = q_31_pad_type_0, strides = var_2487, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_169_cast_fp16)[name = tensor("q_31_cast_fp16")]; + tensor var_2493 = const()[name = tensor("op_2493"), val = tensor([1, 1])]; + tensor var_2495 = const()[name = tensor("op_2495"), val = tensor([1, 1])]; + tensor k_31_pad_type_0 = const()[name = tensor("k_31_pad_type_0"), val = tensor("custom")]; + tensor k_31_pad_0 = const()[name = tensor("k_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(252412864)))]; + tensor k_31_cast_fp16 = conv(dilations = var_2495, groups = var_2306, pad = k_31_pad_0, pad_type = k_31_pad_type_0, strides = var_2493, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_31_cast_fp16")]; + tensor var_2499 = const()[name = tensor("op_2499"), val = tensor([1, 1])]; + tensor var_2501 = const()[name = tensor("op_2501"), val = tensor([1, 1])]; + tensor v_31_pad_type_0 = const()[name = tensor("v_31_pad_type_0"), val = tensor("custom")]; + tensor v_31_pad_0 = const()[name = tensor("v_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(255034368)))]; + tensor v_31_cast_fp16 = conv(dilations = var_2501, groups = var_2306, pad = v_31_pad_0, pad_type = v_31_pad_type_0, strides = var_2499, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_31_cast_fp16")]; + tensor var_2505 = const()[name = tensor("op_2505"), val = tensor([2, 20, 64, -1])]; + tensor var_2506_cast_fp16 = reshape(shape = var_2505, x = q_31_cast_fp16)[name = tensor("op_2506_cast_fp16")]; + tensor var_2507 = const()[name = tensor("op_2507"), val = tensor([2, 20, 64, -1])]; + tensor var_2508_cast_fp16 = reshape(shape = var_2507, x = k_31_cast_fp16)[name = tensor("op_2508_cast_fp16")]; + tensor var_2509 = const()[name = tensor("op_2509"), val = tensor([2, 20, 64, -1])]; + tensor var_2510_cast_fp16 = reshape(shape = var_2509, x = v_31_cast_fp16)[name = tensor("op_2510_cast_fp16")]; + tensor attn_weights_61_transpose_x_0 = const()[name = tensor("attn_weights_61_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_61_transpose_y_0 = const()[name = tensor("attn_weights_61_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_61_cast_fp16 = matmul(transpose_x = attn_weights_61_transpose_x_0, transpose_y = attn_weights_61_transpose_y_0, x = var_2506_cast_fp16, y = var_2508_cast_fp16)[name = tensor("attn_weights_61_cast_fp16")]; + tensor attn_weights_63_cast_fp16 = mul(x = attn_weights_61_cast_fp16, y = var_2297_to_fp16)[name = tensor("attn_weights_63_cast_fp16")]; + tensor var_2514_cast_fp16 = softmax(axis = var_2290, x = attn_weights_63_cast_fp16)[name = tensor("op_2514_cast_fp16")]; + tensor attn_31_transpose_x_0 = const()[name = tensor("attn_31_transpose_x_0"), val = tensor(false)]; + tensor attn_31_transpose_y_0 = const()[name = tensor("attn_31_transpose_y_0"), val = tensor(true)]; + tensor attn_31_cast_fp16 = matmul(transpose_x = attn_31_transpose_x_0, transpose_y = attn_31_transpose_y_0, x = var_2510_cast_fp16, y = var_2514_cast_fp16)[name = tensor("attn_31_cast_fp16")]; + tensor var_2518 = const()[name = tensor("op_2518"), val = tensor([2, 1280, 1, -1])]; + tensor input_303_cast_fp16 = reshape(shape = var_2518, x = attn_31_cast_fp16)[name = tensor("input_303_cast_fp16")]; + tensor var_2523 = const()[name = tensor("op_2523"), val = tensor([1, 1])]; + tensor var_2525 = const()[name = tensor("op_2525"), val = tensor([1, 1])]; + tensor var_2527_pad_type_0 = const()[name = tensor("op_2527_pad_type_0"), val = tensor("custom")]; + tensor var_2527_pad_0 = const()[name = tensor("op_2527_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(257655872)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(260932736)))]; + tensor var_2527_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_2525, groups = var_2306, pad = var_2527_pad_0, pad_type = var_2527_pad_type_0, strides = var_2523, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_303_cast_fp16)[name = tensor("op_2527_cast_fp16")]; + tensor inputs_47_cast_fp16 = add(x = var_2527_cast_fp16, y = inputs_45_cast_fp16)[name = tensor("inputs_47_cast_fp16")]; + tensor var_2531 = const()[name = tensor("op_2531"), val = tensor([1])]; + tensor channels_mean_47_cast_fp16 = reduce_mean(axes = var_2531, keep_dims = var_2301, x = inputs_47_cast_fp16)[name = tensor("channels_mean_47_cast_fp16")]; + tensor zero_mean_47_cast_fp16 = sub(x = inputs_47_cast_fp16, y = channels_mean_47_cast_fp16)[name = tensor("zero_mean_47_cast_fp16")]; + tensor zero_mean_sq_47_cast_fp16 = mul(x = zero_mean_47_cast_fp16, y = zero_mean_47_cast_fp16)[name = tensor("zero_mean_sq_47_cast_fp16")]; + tensor var_2535 = const()[name = tensor("op_2535"), val = tensor([1])]; + tensor var_2536_cast_fp16 = reduce_mean(axes = var_2535, keep_dims = var_2301, x = zero_mean_sq_47_cast_fp16)[name = tensor("op_2536_cast_fp16")]; + tensor var_2537_to_fp16 = const()[name = tensor("op_2537_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2538_cast_fp16 = add(x = var_2536_cast_fp16, y = var_2537_to_fp16)[name = tensor("op_2538_cast_fp16")]; + tensor denom_47_epsilon_0_to_fp16 = const()[name = tensor("denom_47_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_47_cast_fp16 = rsqrt(epsilon = denom_47_epsilon_0_to_fp16, x = var_2538_cast_fp16)[name = tensor("denom_47_cast_fp16")]; + tensor out_47_cast_fp16 = mul(x = zero_mean_47_cast_fp16, y = denom_47_cast_fp16)[name = tensor("out_47_cast_fp16")]; + tensor var_2542_to_fp16 = const()[name = tensor("op_2542_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(260935360)))]; + tensor var_2543_cast_fp16 = add(x = out_47_cast_fp16, y = var_2542_to_fp16)[name = tensor("op_2543_cast_fp16")]; + tensor var_2545_to_fp16 = const()[name = tensor("op_2545_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(260937984)))]; + tensor input_305_cast_fp16 = mul(x = var_2543_cast_fp16, y = var_2545_to_fp16)[name = tensor("input_305_cast_fp16")]; + tensor var_2553 = const()[name = tensor("op_2553"), val = tensor([1, 1])]; + tensor var_2555 = const()[name = tensor("op_2555"), val = tensor([1, 1])]; + tensor var_2557_pad_type_0 = const()[name = tensor("op_2557_pad_type_0"), val = tensor("custom")]; + tensor var_2557_pad_0 = const()[name = tensor("op_2557_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(260940608)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(287155072)))]; + tensor var_2557_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_2555, groups = var_2306, pad = var_2557_pad_0, pad_type = var_2557_pad_type_0, strides = var_2553, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_305_cast_fp16)[name = tensor("op_2557_cast_fp16")]; + tensor var_2558_split_sizes_0 = const()[name = tensor("op_2558_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2558_axis_0 = const()[name = tensor("op_2558_axis_0"), val = tensor(1)]; + tensor var_2558_cast_fp16_0, tensor var_2558_cast_fp16_1 = split(axis = var_2558_axis_0, split_sizes = var_2558_split_sizes_0, x = var_2557_cast_fp16)[name = tensor("op_2558_cast_fp16")]; + tensor var_2560_mode_0 = const()[name = tensor("op_2560_mode_0"), val = tensor("EXACT")]; + tensor var_2560_cast_fp16 = gelu(mode = var_2560_mode_0, x = var_2558_cast_fp16_1)[name = tensor("op_2560_cast_fp16")]; + tensor input_307_cast_fp16 = mul(x = var_2558_cast_fp16_0, y = var_2560_cast_fp16)[name = tensor("input_307_cast_fp16")]; + tensor var_2564 = const()[name = tensor("op_2564"), val = tensor([1, 1])]; + tensor var_2566 = const()[name = tensor("op_2566"), val = tensor([1, 1])]; + tensor var_2568_pad_type_0 = const()[name = tensor("op_2568_pad_type_0"), val = tensor("custom")]; + tensor var_2568_pad_0 = const()[name = tensor("op_2568_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(287175616)))]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(300282880)))]; + tensor var_2568_cast_fp16 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_2566, groups = var_2306, pad = var_2568_pad_0, pad_type = var_2568_pad_type_0, strides = var_2564, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_307_cast_fp16)[name = tensor("op_2568_cast_fp16")]; + tensor hidden_states_173_cast_fp16 = add(x = var_2568_cast_fp16, y = inputs_47_cast_fp16)[name = tensor("hidden_states_173_cast_fp16")]; + tensor var_2570 = const()[name = tensor("op_2570"), val = tensor([2, 1280, 12, 20])]; + tensor input_309_cast_fp16 = reshape(shape = var_2570, x = hidden_states_173_cast_fp16)[name = tensor("input_309_cast_fp16")]; + tensor var_2574 = const()[name = tensor("op_2574"), val = tensor([1, 1])]; + tensor var_2576 = const()[name = tensor("op_2576"), val = tensor([1, 1])]; + tensor hidden_states_175_pad_type_0 = const()[name = tensor("hidden_states_175_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_175_pad_0 = const()[name = tensor("hidden_states_175_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(300285504)))]; + tensor up_blocks_1_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303562368)))]; + tensor hidden_states_175_cast_fp16 = conv(bias = up_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_2576, groups = var_2306, pad = hidden_states_175_pad_0, pad_type = hidden_states_175_pad_type_0, strides = var_2574, weight = up_blocks_1_attentions_0_proj_out_weight_to_fp16, x = input_309_cast_fp16)[name = tensor("hidden_states_175_cast_fp16")]; + tensor hidden_states_177_cast_fp16 = add(x = hidden_states_175_cast_fp16, y = hidden_states_163_cast_fp16)[name = tensor("hidden_states_177_cast_fp16")]; + tensor input_311_interleave_0 = const()[name = tensor("input_311_interleave_0"), val = tensor(false)]; + tensor cast_5 = cast(dtype = cast_2_dtype_0, x = input_143_cast_fp16)[name = tensor("cast_5")]; + tensor input_311_cast_fp16 = concat(axis = var_2306, interleave = input_311_interleave_0, values = (hidden_states_177_cast_fp16, cast_5))[name = tensor("input_311_cast_fp16")]; + tensor reshape_144_shape_0 = const()[name = tensor("reshape_144_shape_0"), val = tensor([2, 32, 80, 12, 20])]; + tensor reshape_144_cast_fp16 = reshape(shape = reshape_144_shape_0, x = input_311_cast_fp16)[name = tensor("reshape_144_cast_fp16")]; + tensor reduce_mean_108_axes_0 = const()[name = tensor("reduce_mean_108_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_108_keep_dims_0 = const()[name = tensor("reduce_mean_108_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_108_cast_fp16 = reduce_mean(axes = reduce_mean_108_axes_0, keep_dims = reduce_mean_108_keep_dims_0, x = reshape_144_cast_fp16)[name = tensor("reduce_mean_108_cast_fp16")]; + tensor sub_72_cast_fp16 = sub(x = reshape_144_cast_fp16, y = reduce_mean_108_cast_fp16)[name = tensor("sub_72_cast_fp16")]; + tensor square_36_cast_fp16 = square(x = sub_72_cast_fp16)[name = tensor("square_36_cast_fp16")]; + tensor reduce_mean_110_axes_0 = const()[name = tensor("reduce_mean_110_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_110_keep_dims_0 = const()[name = tensor("reduce_mean_110_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_110_cast_fp16 = reduce_mean(axes = reduce_mean_110_axes_0, keep_dims = reduce_mean_110_keep_dims_0, x = square_36_cast_fp16)[name = tensor("reduce_mean_110_cast_fp16")]; + tensor add_72_y_0_to_fp16 = const()[name = tensor("add_72_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_72_cast_fp16 = add(x = reduce_mean_110_cast_fp16, y = add_72_y_0_to_fp16)[name = tensor("add_72_cast_fp16")]; + tensor sqrt_36_cast_fp16 = sqrt(x = add_72_cast_fp16)[name = tensor("sqrt_36_cast_fp16")]; + tensor real_div_36_cast_fp16 = real_div(x = sub_72_cast_fp16, y = sqrt_36_cast_fp16)[name = tensor("real_div_36_cast_fp16")]; + tensor reshape_145_shape_0 = const()[name = tensor("reshape_145_shape_0"), val = tensor([2, 2560, 12, 20])]; + tensor reshape_145_cast_fp16 = reshape(shape = reshape_145_shape_0, x = real_div_36_cast_fp16)[name = tensor("reshape_145_cast_fp16")]; + tensor add_73_gamma_0_to_fp16 = const()[name = tensor("add_73_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303564992)))]; + tensor add_73_beta_0_to_fp16 = const()[name = tensor("add_73_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303570176)))]; + tensor add_73_epsilon_0_to_fp16 = const()[name = tensor("add_73_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_73_cast_fp16 = batch_norm(beta = add_73_beta_0_to_fp16, epsilon = add_73_epsilon_0_to_fp16, gamma = add_73_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_145_cast_fp16)[name = tensor("add_73_cast_fp16")]; + tensor input_315_cast_fp16 = silu(x = add_73_cast_fp16)[name = tensor("input_315_cast_fp16")]; + tensor var_2594 = const()[name = tensor("op_2594"), val = tensor([1, 1])]; + tensor var_2596 = const()[name = tensor("op_2596"), val = tensor([1, 1])]; + tensor hidden_states_179_pad_type_0 = const()[name = tensor("hidden_states_179_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_179_pad_0 = const()[name = tensor("hidden_states_179_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303575360)))]; + tensor up_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362557824)))]; + tensor hidden_states_179_cast_fp16 = conv(bias = up_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_2596, groups = var_2306, pad = hidden_states_179_pad_0, pad_type = hidden_states_179_pad_type_0, strides = var_2594, weight = up_blocks_1_resnets_1_conv1_weight_to_fp16, x = input_315_cast_fp16)[name = tensor("hidden_states_179_cast_fp16")]; + tensor var_2602 = const()[name = tensor("op_2602"), val = tensor([1, 1])]; + tensor var_2604 = const()[name = tensor("op_2604"), val = tensor([1, 1])]; + tensor temb_29_pad_type_0 = const()[name = tensor("temb_29_pad_type_0"), val = tensor("custom")]; + tensor temb_29_pad_0 = const()[name = tensor("temb_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(362560448)))]; + tensor up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(365837312)))]; + tensor temb_29_cast_fp16 = conv(bias = up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_2604, groups = var_2306, pad = temb_29_pad_0, pad_type = temb_29_pad_type_0, strides = var_2602, weight = up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16, x = cast_12)[name = tensor("temb_29_cast_fp16")]; + tensor input_319_cast_fp16 = add(x = hidden_states_179_cast_fp16, y = temb_29_cast_fp16)[name = tensor("input_319_cast_fp16")]; + tensor reshape_148_shape_0 = const()[name = tensor("reshape_148_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_148_cast_fp16 = reshape(shape = reshape_148_shape_0, x = input_319_cast_fp16)[name = tensor("reshape_148_cast_fp16")]; + tensor reduce_mean_111_axes_0 = const()[name = tensor("reduce_mean_111_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_111_keep_dims_0 = const()[name = tensor("reduce_mean_111_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_111_cast_fp16 = reduce_mean(axes = reduce_mean_111_axes_0, keep_dims = reduce_mean_111_keep_dims_0, x = reshape_148_cast_fp16)[name = tensor("reduce_mean_111_cast_fp16")]; + tensor sub_74_cast_fp16 = sub(x = reshape_148_cast_fp16, y = reduce_mean_111_cast_fp16)[name = tensor("sub_74_cast_fp16")]; + tensor square_37_cast_fp16 = square(x = sub_74_cast_fp16)[name = tensor("square_37_cast_fp16")]; + tensor reduce_mean_113_axes_0 = const()[name = tensor("reduce_mean_113_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_113_keep_dims_0 = const()[name = tensor("reduce_mean_113_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_113_cast_fp16 = reduce_mean(axes = reduce_mean_113_axes_0, keep_dims = reduce_mean_113_keep_dims_0, x = square_37_cast_fp16)[name = tensor("reduce_mean_113_cast_fp16")]; + tensor add_74_y_0_to_fp16 = const()[name = tensor("add_74_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_74_cast_fp16 = add(x = reduce_mean_113_cast_fp16, y = add_74_y_0_to_fp16)[name = tensor("add_74_cast_fp16")]; + tensor sqrt_37_cast_fp16 = sqrt(x = add_74_cast_fp16)[name = tensor("sqrt_37_cast_fp16")]; + tensor real_div_37_cast_fp16 = real_div(x = sub_74_cast_fp16, y = sqrt_37_cast_fp16)[name = tensor("real_div_37_cast_fp16")]; + tensor reshape_149_shape_0 = const()[name = tensor("reshape_149_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_149_cast_fp16 = reshape(shape = reshape_149_shape_0, x = real_div_37_cast_fp16)[name = tensor("reshape_149_cast_fp16")]; + tensor add_75_gamma_0_to_fp16 = const()[name = tensor("add_75_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(365839936)))]; + tensor add_75_beta_0_to_fp16 = const()[name = tensor("add_75_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(365842560)))]; + tensor add_75_epsilon_0_to_fp16 = const()[name = tensor("add_75_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_75_cast_fp16 = batch_norm(beta = add_75_beta_0_to_fp16, epsilon = add_75_epsilon_0_to_fp16, gamma = add_75_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_149_cast_fp16)[name = tensor("add_75_cast_fp16")]; + tensor input_323_cast_fp16 = silu(x = add_75_cast_fp16)[name = tensor("input_323_cast_fp16")]; + tensor var_2614 = const()[name = tensor("op_2614"), val = tensor([1, 1])]; + tensor var_2616 = const()[name = tensor("op_2616"), val = tensor([1, 1])]; + tensor hidden_states_181_pad_type_0 = const()[name = tensor("hidden_states_181_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_181_pad_0 = const()[name = tensor("hidden_states_181_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(365845184)))]; + tensor up_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(395336448)))]; + tensor hidden_states_181_cast_fp16 = conv(bias = up_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_2616, groups = var_2306, pad = hidden_states_181_pad_0, pad_type = hidden_states_181_pad_type_0, strides = var_2614, weight = up_blocks_1_resnets_1_conv2_weight_to_fp16, x = input_323_cast_fp16)[name = tensor("hidden_states_181_cast_fp16")]; + tensor var_2621 = const()[name = tensor("op_2621"), val = tensor([1, 1])]; + tensor var_2623 = const()[name = tensor("op_2623"), val = tensor([1, 1])]; + 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([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(395339072)))]; + tensor up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(401892736)))]; + tensor x_13_cast_fp16 = conv(bias = up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_2623, groups = var_2306, pad = x_13_pad_0, pad_type = x_13_pad_type_0, strides = var_2621, weight = up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16, x = input_311_cast_fp16)[name = tensor("x_13_cast_fp16")]; + tensor hidden_states_183_cast_fp16 = add(x = x_13_cast_fp16, y = hidden_states_181_cast_fp16)[name = tensor("hidden_states_183_cast_fp16")]; + tensor reshape_152_shape_0 = const()[name = tensor("reshape_152_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_152_cast_fp16 = reshape(shape = reshape_152_shape_0, x = hidden_states_183_cast_fp16)[name = tensor("reshape_152_cast_fp16")]; + tensor reduce_mean_114_axes_0 = const()[name = tensor("reduce_mean_114_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_114_keep_dims_0 = const()[name = tensor("reduce_mean_114_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_114_cast_fp16 = reduce_mean(axes = reduce_mean_114_axes_0, keep_dims = reduce_mean_114_keep_dims_0, x = reshape_152_cast_fp16)[name = tensor("reduce_mean_114_cast_fp16")]; + tensor sub_76_cast_fp16 = sub(x = reshape_152_cast_fp16, y = reduce_mean_114_cast_fp16)[name = tensor("sub_76_cast_fp16")]; + tensor square_38_cast_fp16 = square(x = sub_76_cast_fp16)[name = tensor("square_38_cast_fp16")]; + tensor reduce_mean_116_axes_0 = const()[name = tensor("reduce_mean_116_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_116_keep_dims_0 = const()[name = tensor("reduce_mean_116_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_116_cast_fp16 = reduce_mean(axes = reduce_mean_116_axes_0, keep_dims = reduce_mean_116_keep_dims_0, x = square_38_cast_fp16)[name = tensor("reduce_mean_116_cast_fp16")]; + tensor add_76_y_0_to_fp16 = const()[name = tensor("add_76_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_76_cast_fp16 = add(x = reduce_mean_116_cast_fp16, y = add_76_y_0_to_fp16)[name = tensor("add_76_cast_fp16")]; + tensor sqrt_38_cast_fp16 = sqrt(x = add_76_cast_fp16)[name = tensor("sqrt_38_cast_fp16")]; + tensor real_div_38_cast_fp16 = real_div(x = sub_76_cast_fp16, y = sqrt_38_cast_fp16)[name = tensor("real_div_38_cast_fp16")]; + tensor reshape_153_shape_0 = const()[name = tensor("reshape_153_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_153_cast_fp16 = reshape(shape = reshape_153_shape_0, x = real_div_38_cast_fp16)[name = tensor("reshape_153_cast_fp16")]; + tensor add_77_gamma_0_to_fp16 = const()[name = tensor("add_77_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(401895360)))]; + tensor add_77_beta_0_to_fp16 = const()[name = tensor("add_77_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(401897984)))]; + tensor add_77_epsilon_0_to_fp16 = const()[name = tensor("add_77_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_77_cast_fp16 = batch_norm(beta = add_77_beta_0_to_fp16, epsilon = add_77_epsilon_0_to_fp16, gamma = add_77_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_153_cast_fp16)[name = tensor("add_77_cast_fp16")]; + tensor var_2643 = const()[name = tensor("op_2643"), val = tensor([1, 1])]; + tensor var_2645 = const()[name = tensor("op_2645"), val = tensor([1, 1])]; + tensor hidden_states_185_pad_type_0 = const()[name = tensor("hidden_states_185_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_185_pad_0 = const()[name = tensor("hidden_states_185_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(401900608)))]; + tensor up_blocks_1_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405177472)))]; + tensor hidden_states_185_cast_fp16 = conv(bias = up_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_2645, groups = var_2306, pad = hidden_states_185_pad_0, pad_type = hidden_states_185_pad_type_0, strides = var_2643, weight = up_blocks_1_attentions_1_proj_in_weight_to_fp16, x = add_77_cast_fp16)[name = tensor("hidden_states_185_cast_fp16")]; + tensor var_2650 = const()[name = tensor("op_2650"), val = tensor([2, 1280, 1, 240])]; + tensor inputs_49_cast_fp16 = reshape(shape = var_2650, x = hidden_states_185_cast_fp16)[name = tensor("inputs_49_cast_fp16")]; + tensor var_2660 = const()[name = tensor("op_2660"), val = tensor([1])]; + tensor channels_mean_49_cast_fp16 = reduce_mean(axes = var_2660, keep_dims = var_2301, x = inputs_49_cast_fp16)[name = tensor("channels_mean_49_cast_fp16")]; + tensor zero_mean_49_cast_fp16 = sub(x = inputs_49_cast_fp16, y = channels_mean_49_cast_fp16)[name = tensor("zero_mean_49_cast_fp16")]; + tensor zero_mean_sq_49_cast_fp16 = mul(x = zero_mean_49_cast_fp16, y = zero_mean_49_cast_fp16)[name = tensor("zero_mean_sq_49_cast_fp16")]; + tensor var_2664 = const()[name = tensor("op_2664"), val = tensor([1])]; + tensor var_2665_cast_fp16 = reduce_mean(axes = var_2664, keep_dims = var_2301, x = zero_mean_sq_49_cast_fp16)[name = tensor("op_2665_cast_fp16")]; + tensor var_2666_to_fp16 = const()[name = tensor("op_2666_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2667_cast_fp16 = add(x = var_2665_cast_fp16, y = var_2666_to_fp16)[name = tensor("op_2667_cast_fp16")]; + tensor denom_49_epsilon_0_to_fp16 = const()[name = tensor("denom_49_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_49_cast_fp16 = rsqrt(epsilon = denom_49_epsilon_0_to_fp16, x = var_2667_cast_fp16)[name = tensor("denom_49_cast_fp16")]; + tensor out_49_cast_fp16 = mul(x = zero_mean_49_cast_fp16, y = denom_49_cast_fp16)[name = tensor("out_49_cast_fp16")]; + tensor var_2671_to_fp16 = const()[name = tensor("op_2671_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405180096)))]; + tensor var_2672_cast_fp16 = add(x = out_49_cast_fp16, y = var_2671_to_fp16)[name = tensor("op_2672_cast_fp16")]; + tensor var_2674_to_fp16 = const()[name = tensor("op_2674_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405182720)))]; + tensor hidden_states_187_cast_fp16 = mul(x = var_2672_cast_fp16, y = var_2674_to_fp16)[name = tensor("hidden_states_187_cast_fp16")]; + tensor var_2681 = const()[name = tensor("op_2681"), val = tensor([1, 1])]; + tensor var_2683 = const()[name = tensor("op_2683"), val = tensor([1, 1])]; + tensor q_33_pad_type_0 = const()[name = tensor("q_33_pad_type_0"), val = tensor("custom")]; + tensor q_33_pad_0 = const()[name = tensor("q_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405185344)))]; + tensor q_33_cast_fp16 = conv(dilations = var_2683, groups = var_2306, pad = q_33_pad_0, pad_type = q_33_pad_type_0, strides = var_2681, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_187_cast_fp16)[name = tensor("q_33_cast_fp16")]; + tensor var_2687 = const()[name = tensor("op_2687"), val = tensor([1, 1])]; + tensor var_2689 = const()[name = tensor("op_2689"), val = tensor([1, 1])]; + tensor k_33_pad_type_0 = const()[name = tensor("k_33_pad_type_0"), val = tensor("custom")]; + tensor k_33_pad_0 = const()[name = tensor("k_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(408462208)))]; + tensor k_33_cast_fp16 = conv(dilations = var_2689, groups = var_2306, pad = k_33_pad_0, pad_type = k_33_pad_type_0, strides = var_2687, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_187_cast_fp16)[name = tensor("k_33_cast_fp16")]; + tensor var_2693 = const()[name = tensor("op_2693"), val = tensor([1, 1])]; + tensor var_2695 = const()[name = tensor("op_2695"), val = tensor([1, 1])]; + tensor v_33_pad_type_0 = const()[name = tensor("v_33_pad_type_0"), val = tensor("custom")]; + tensor v_33_pad_0 = const()[name = tensor("v_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(411739072)))]; + tensor v_33_cast_fp16 = conv(dilations = var_2695, groups = var_2306, pad = v_33_pad_0, pad_type = v_33_pad_type_0, strides = var_2693, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_187_cast_fp16)[name = tensor("v_33_cast_fp16")]; + tensor var_2699 = const()[name = tensor("op_2699"), val = tensor([2, 20, 64, -1])]; + tensor var_2700_cast_fp16 = reshape(shape = var_2699, x = q_33_cast_fp16)[name = tensor("op_2700_cast_fp16")]; + tensor var_2701 = const()[name = tensor("op_2701"), val = tensor([2, 20, 64, -1])]; + tensor var_2702_cast_fp16 = reshape(shape = var_2701, x = k_33_cast_fp16)[name = tensor("op_2702_cast_fp16")]; + tensor var_2703 = const()[name = tensor("op_2703"), val = tensor([2, 20, 64, -1])]; + tensor var_2704_cast_fp16 = reshape(shape = var_2703, x = v_33_cast_fp16)[name = tensor("op_2704_cast_fp16")]; + tensor attn_weights_65_transpose_x_0 = const()[name = tensor("attn_weights_65_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_65_transpose_y_0 = const()[name = tensor("attn_weights_65_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_65_cast_fp16 = matmul(transpose_x = attn_weights_65_transpose_x_0, transpose_y = attn_weights_65_transpose_y_0, x = var_2700_cast_fp16, y = var_2702_cast_fp16)[name = tensor("attn_weights_65_cast_fp16")]; + tensor attn_weights_67_cast_fp16 = mul(x = attn_weights_65_cast_fp16, y = var_2297_to_fp16)[name = tensor("attn_weights_67_cast_fp16")]; + tensor var_2708_cast_fp16 = softmax(axis = var_2290, x = attn_weights_67_cast_fp16)[name = tensor("op_2708_cast_fp16")]; + tensor attn_33_transpose_x_0 = const()[name = tensor("attn_33_transpose_x_0"), val = tensor(false)]; + tensor attn_33_transpose_y_0 = const()[name = tensor("attn_33_transpose_y_0"), val = tensor(true)]; + tensor attn_33_cast_fp16 = matmul(transpose_x = attn_33_transpose_x_0, transpose_y = attn_33_transpose_y_0, x = var_2704_cast_fp16, y = var_2708_cast_fp16)[name = tensor("attn_33_cast_fp16")]; + tensor var_2712 = const()[name = tensor("op_2712"), val = tensor([2, 1280, 1, -1])]; + tensor input_327_cast_fp16 = reshape(shape = var_2712, x = attn_33_cast_fp16)[name = tensor("input_327_cast_fp16")]; + tensor var_2717 = const()[name = tensor("op_2717"), val = tensor([1, 1])]; + tensor var_2719 = const()[name = tensor("op_2719"), val = tensor([1, 1])]; + tensor var_2721_pad_type_0 = const()[name = tensor("op_2721_pad_type_0"), val = tensor("custom")]; + tensor var_2721_pad_0 = const()[name = tensor("op_2721_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(415015936)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418292800)))]; + tensor var_2721_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_2719, groups = var_2306, pad = var_2721_pad_0, pad_type = var_2721_pad_type_0, strides = var_2717, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_327_cast_fp16)[name = tensor("op_2721_cast_fp16")]; + tensor inputs_51_cast_fp16 = add(x = var_2721_cast_fp16, y = inputs_49_cast_fp16)[name = tensor("inputs_51_cast_fp16")]; + tensor var_2725 = const()[name = tensor("op_2725"), val = tensor([1])]; + tensor channels_mean_51_cast_fp16 = reduce_mean(axes = var_2725, keep_dims = var_2301, x = inputs_51_cast_fp16)[name = tensor("channels_mean_51_cast_fp16")]; + tensor zero_mean_51_cast_fp16 = sub(x = inputs_51_cast_fp16, y = channels_mean_51_cast_fp16)[name = tensor("zero_mean_51_cast_fp16")]; + tensor zero_mean_sq_51_cast_fp16 = mul(x = zero_mean_51_cast_fp16, y = zero_mean_51_cast_fp16)[name = tensor("zero_mean_sq_51_cast_fp16")]; + tensor var_2729 = const()[name = tensor("op_2729"), val = tensor([1])]; + tensor var_2730_cast_fp16 = reduce_mean(axes = var_2729, keep_dims = var_2301, x = zero_mean_sq_51_cast_fp16)[name = tensor("op_2730_cast_fp16")]; + tensor var_2731_to_fp16 = const()[name = tensor("op_2731_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2732_cast_fp16 = add(x = var_2730_cast_fp16, y = var_2731_to_fp16)[name = tensor("op_2732_cast_fp16")]; + tensor denom_51_epsilon_0_to_fp16 = const()[name = tensor("denom_51_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_51_cast_fp16 = rsqrt(epsilon = denom_51_epsilon_0_to_fp16, x = var_2732_cast_fp16)[name = tensor("denom_51_cast_fp16")]; + tensor out_51_cast_fp16 = mul(x = zero_mean_51_cast_fp16, y = denom_51_cast_fp16)[name = tensor("out_51_cast_fp16")]; + tensor var_2736_to_fp16 = const()[name = tensor("op_2736_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418295424)))]; + tensor var_2737_cast_fp16 = add(x = out_51_cast_fp16, y = var_2736_to_fp16)[name = tensor("op_2737_cast_fp16")]; + tensor var_2739_to_fp16 = const()[name = tensor("op_2739_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418298048)))]; + tensor hidden_states_189_cast_fp16 = mul(x = var_2737_cast_fp16, y = var_2739_to_fp16)[name = tensor("hidden_states_189_cast_fp16")]; + tensor var_2746 = const()[name = tensor("op_2746"), val = tensor([1, 1])]; + tensor var_2748 = const()[name = tensor("op_2748"), val = tensor([1, 1])]; + tensor q_35_pad_type_0 = const()[name = tensor("q_35_pad_type_0"), val = tensor("custom")]; + tensor q_35_pad_0 = const()[name = tensor("q_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(418300672)))]; + tensor q_35_cast_fp16 = conv(dilations = var_2748, groups = var_2306, pad = q_35_pad_0, pad_type = q_35_pad_type_0, strides = var_2746, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_189_cast_fp16)[name = tensor("q_35_cast_fp16")]; + tensor var_2752 = const()[name = tensor("op_2752"), val = tensor([1, 1])]; + tensor var_2754 = const()[name = tensor("op_2754"), val = tensor([1, 1])]; + tensor k_35_pad_type_0 = const()[name = tensor("k_35_pad_type_0"), val = tensor("custom")]; + tensor k_35_pad_0 = const()[name = tensor("k_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(421577536)))]; + tensor k_35_cast_fp16 = conv(dilations = var_2754, groups = var_2306, pad = k_35_pad_0, pad_type = k_35_pad_type_0, strides = var_2752, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_35_cast_fp16")]; + tensor var_2758 = const()[name = tensor("op_2758"), val = tensor([1, 1])]; + tensor var_2760 = const()[name = tensor("op_2760"), val = tensor([1, 1])]; + tensor v_35_pad_type_0 = const()[name = tensor("v_35_pad_type_0"), val = tensor("custom")]; + tensor v_35_pad_0 = const()[name = tensor("v_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(424199040)))]; + tensor v_35_cast_fp16 = conv(dilations = var_2760, groups = var_2306, pad = v_35_pad_0, pad_type = v_35_pad_type_0, strides = var_2758, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_35_cast_fp16")]; + tensor var_2764 = const()[name = tensor("op_2764"), val = tensor([2, 20, 64, -1])]; + tensor var_2765_cast_fp16 = reshape(shape = var_2764, x = q_35_cast_fp16)[name = tensor("op_2765_cast_fp16")]; + tensor var_2766 = const()[name = tensor("op_2766"), val = tensor([2, 20, 64, -1])]; + tensor var_2767_cast_fp16 = reshape(shape = var_2766, x = k_35_cast_fp16)[name = tensor("op_2767_cast_fp16")]; + tensor var_2768 = const()[name = tensor("op_2768"), val = tensor([2, 20, 64, -1])]; + tensor var_2769_cast_fp16 = reshape(shape = var_2768, x = v_35_cast_fp16)[name = tensor("op_2769_cast_fp16")]; + tensor attn_weights_69_transpose_x_0 = const()[name = tensor("attn_weights_69_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_69_transpose_y_0 = const()[name = tensor("attn_weights_69_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_69_cast_fp16 = matmul(transpose_x = attn_weights_69_transpose_x_0, transpose_y = attn_weights_69_transpose_y_0, x = var_2765_cast_fp16, y = var_2767_cast_fp16)[name = tensor("attn_weights_69_cast_fp16")]; + tensor attn_weights_71_cast_fp16 = mul(x = attn_weights_69_cast_fp16, y = var_2297_to_fp16)[name = tensor("attn_weights_71_cast_fp16")]; + tensor var_2773_cast_fp16 = softmax(axis = var_2290, x = attn_weights_71_cast_fp16)[name = tensor("op_2773_cast_fp16")]; + tensor attn_35_transpose_x_0 = const()[name = tensor("attn_35_transpose_x_0"), val = tensor(false)]; + tensor attn_35_transpose_y_0 = const()[name = tensor("attn_35_transpose_y_0"), val = tensor(true)]; + tensor attn_35_cast_fp16 = matmul(transpose_x = attn_35_transpose_x_0, transpose_y = attn_35_transpose_y_0, x = var_2769_cast_fp16, y = var_2773_cast_fp16)[name = tensor("attn_35_cast_fp16")]; + tensor var_2777 = const()[name = tensor("op_2777"), val = tensor([2, 1280, 1, -1])]; + tensor input_329_cast_fp16 = reshape(shape = var_2777, x = attn_35_cast_fp16)[name = tensor("input_329_cast_fp16")]; + tensor var_2782 = const()[name = tensor("op_2782"), val = tensor([1, 1])]; + tensor var_2784 = const()[name = tensor("op_2784"), val = tensor([1, 1])]; + tensor var_2786_pad_type_0 = const()[name = tensor("op_2786_pad_type_0"), val = tensor("custom")]; + tensor var_2786_pad_0 = const()[name = tensor("op_2786_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426820544)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(430097408)))]; + tensor var_2786_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_2784, groups = var_2306, pad = var_2786_pad_0, pad_type = var_2786_pad_type_0, strides = var_2782, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_329_cast_fp16)[name = tensor("op_2786_cast_fp16")]; + tensor inputs_53_cast_fp16 = add(x = var_2786_cast_fp16, y = inputs_51_cast_fp16)[name = tensor("inputs_53_cast_fp16")]; + tensor var_2790 = const()[name = tensor("op_2790"), val = tensor([1])]; + tensor channels_mean_53_cast_fp16 = reduce_mean(axes = var_2790, keep_dims = var_2301, x = inputs_53_cast_fp16)[name = tensor("channels_mean_53_cast_fp16")]; + tensor zero_mean_53_cast_fp16 = sub(x = inputs_53_cast_fp16, y = channels_mean_53_cast_fp16)[name = tensor("zero_mean_53_cast_fp16")]; + tensor zero_mean_sq_53_cast_fp16 = mul(x = zero_mean_53_cast_fp16, y = zero_mean_53_cast_fp16)[name = tensor("zero_mean_sq_53_cast_fp16")]; + tensor var_2794 = const()[name = tensor("op_2794"), val = tensor([1])]; + tensor var_2795_cast_fp16 = reduce_mean(axes = var_2794, keep_dims = var_2301, x = zero_mean_sq_53_cast_fp16)[name = tensor("op_2795_cast_fp16")]; + tensor var_2796_to_fp16 = const()[name = tensor("op_2796_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2797_cast_fp16 = add(x = var_2795_cast_fp16, y = var_2796_to_fp16)[name = tensor("op_2797_cast_fp16")]; + tensor denom_53_epsilon_0_to_fp16 = const()[name = tensor("denom_53_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_53_cast_fp16 = rsqrt(epsilon = denom_53_epsilon_0_to_fp16, x = var_2797_cast_fp16)[name = tensor("denom_53_cast_fp16")]; + tensor out_53_cast_fp16 = mul(x = zero_mean_53_cast_fp16, y = denom_53_cast_fp16)[name = tensor("out_53_cast_fp16")]; + tensor var_2801_to_fp16 = const()[name = tensor("op_2801_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(430100032)))]; + tensor var_2802_cast_fp16 = add(x = out_53_cast_fp16, y = var_2801_to_fp16)[name = tensor("op_2802_cast_fp16")]; + tensor var_2804_to_fp16 = const()[name = tensor("op_2804_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(430102656)))]; + tensor input_331_cast_fp16 = mul(x = var_2802_cast_fp16, y = var_2804_to_fp16)[name = tensor("input_331_cast_fp16")]; + tensor var_2812 = const()[name = tensor("op_2812"), val = tensor([1, 1])]; + tensor var_2814 = const()[name = tensor("op_2814"), val = tensor([1, 1])]; + tensor var_2816_pad_type_0 = const()[name = tensor("op_2816_pad_type_0"), val = tensor("custom")]; + tensor var_2816_pad_0 = const()[name = tensor("op_2816_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(430105280)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(456319744)))]; + tensor var_2816_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_2814, groups = var_2306, pad = var_2816_pad_0, pad_type = var_2816_pad_type_0, strides = var_2812, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_331_cast_fp16)[name = tensor("op_2816_cast_fp16")]; + tensor var_2817_split_sizes_0 = const()[name = tensor("op_2817_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2817_axis_0 = const()[name = tensor("op_2817_axis_0"), val = tensor(1)]; + tensor var_2817_cast_fp16_0, tensor var_2817_cast_fp16_1 = split(axis = var_2817_axis_0, split_sizes = var_2817_split_sizes_0, x = var_2816_cast_fp16)[name = tensor("op_2817_cast_fp16")]; + tensor var_2819_mode_0 = const()[name = tensor("op_2819_mode_0"), val = tensor("EXACT")]; + tensor var_2819_cast_fp16 = gelu(mode = var_2819_mode_0, x = var_2817_cast_fp16_1)[name = tensor("op_2819_cast_fp16")]; + tensor input_333_cast_fp16 = mul(x = var_2817_cast_fp16_0, y = var_2819_cast_fp16)[name = tensor("input_333_cast_fp16")]; + tensor var_2823 = const()[name = tensor("op_2823"), val = tensor([1, 1])]; + tensor var_2825 = const()[name = tensor("op_2825"), val = tensor([1, 1])]; + tensor var_2827_pad_type_0 = const()[name = tensor("op_2827_pad_type_0"), val = tensor("custom")]; + tensor var_2827_pad_0 = const()[name = tensor("op_2827_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(456340288)))]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(469447552)))]; + tensor var_2827_cast_fp16 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_2825, groups = var_2306, pad = var_2827_pad_0, pad_type = var_2827_pad_type_0, strides = var_2823, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_333_cast_fp16)[name = tensor("op_2827_cast_fp16")]; + tensor hidden_states_193_cast_fp16 = add(x = var_2827_cast_fp16, y = inputs_53_cast_fp16)[name = tensor("hidden_states_193_cast_fp16")]; + tensor var_2829 = const()[name = tensor("op_2829"), val = tensor([2, 1280, 12, 20])]; + tensor input_335_cast_fp16 = reshape(shape = var_2829, x = hidden_states_193_cast_fp16)[name = tensor("input_335_cast_fp16")]; + tensor var_2833 = const()[name = tensor("op_2833"), val = tensor([1, 1])]; + tensor var_2835 = const()[name = tensor("op_2835"), val = tensor([1, 1])]; + tensor hidden_states_195_pad_type_0 = const()[name = tensor("hidden_states_195_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_195_pad_0 = const()[name = tensor("hidden_states_195_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(469450176)))]; + tensor up_blocks_1_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(472727040)))]; + tensor hidden_states_195_cast_fp16 = conv(bias = up_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_2835, groups = var_2306, pad = hidden_states_195_pad_0, pad_type = hidden_states_195_pad_type_0, strides = var_2833, weight = up_blocks_1_attentions_1_proj_out_weight_to_fp16, x = input_335_cast_fp16)[name = tensor("hidden_states_195_cast_fp16")]; + tensor hidden_states_197_cast_fp16 = add(x = hidden_states_195_cast_fp16, y = hidden_states_183_cast_fp16)[name = tensor("hidden_states_197_cast_fp16")]; + tensor input_337_interleave_0 = const()[name = tensor("input_337_interleave_0"), val = tensor(false)]; + tensor cast_6 = cast(dtype = cast_11_dtype_0, x = input_117_cast_fp16)[name = tensor("cast_6")]; + tensor input_337_cast_fp16 = concat(axis = var_2306, interleave = input_337_interleave_0, values = (hidden_states_197_cast_fp16, cast_6))[name = tensor("input_337_cast_fp16")]; + tensor reshape_156_shape_0 = const()[name = tensor("reshape_156_shape_0"), val = tensor([2, 32, 60, 12, 20])]; + tensor reshape_156_cast_fp16 = reshape(shape = reshape_156_shape_0, x = input_337_cast_fp16)[name = tensor("reshape_156_cast_fp16")]; + tensor reduce_mean_117_axes_0 = const()[name = tensor("reduce_mean_117_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_117_keep_dims_0 = const()[name = tensor("reduce_mean_117_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_117_cast_fp16 = reduce_mean(axes = reduce_mean_117_axes_0, keep_dims = reduce_mean_117_keep_dims_0, x = reshape_156_cast_fp16)[name = tensor("reduce_mean_117_cast_fp16")]; + tensor sub_78_cast_fp16 = sub(x = reshape_156_cast_fp16, y = reduce_mean_117_cast_fp16)[name = tensor("sub_78_cast_fp16")]; + tensor square_39_cast_fp16 = square(x = sub_78_cast_fp16)[name = tensor("square_39_cast_fp16")]; + tensor reduce_mean_119_axes_0 = const()[name = tensor("reduce_mean_119_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_119_keep_dims_0 = const()[name = tensor("reduce_mean_119_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_119_cast_fp16 = reduce_mean(axes = reduce_mean_119_axes_0, keep_dims = reduce_mean_119_keep_dims_0, x = square_39_cast_fp16)[name = tensor("reduce_mean_119_cast_fp16")]; + tensor add_78_y_0_to_fp16 = const()[name = tensor("add_78_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_78_cast_fp16 = add(x = reduce_mean_119_cast_fp16, y = add_78_y_0_to_fp16)[name = tensor("add_78_cast_fp16")]; + tensor sqrt_39_cast_fp16 = sqrt(x = add_78_cast_fp16)[name = tensor("sqrt_39_cast_fp16")]; + tensor real_div_39_cast_fp16 = real_div(x = sub_78_cast_fp16, y = sqrt_39_cast_fp16)[name = tensor("real_div_39_cast_fp16")]; + tensor reshape_157_shape_0 = const()[name = tensor("reshape_157_shape_0"), val = tensor([2, 1920, 12, 20])]; + tensor reshape_157_cast_fp16 = reshape(shape = reshape_157_shape_0, x = real_div_39_cast_fp16)[name = tensor("reshape_157_cast_fp16")]; + tensor add_79_mean_0_to_fp16 = const()[name = tensor("add_79_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(472729664)))]; + tensor add_79_variance_0_to_fp16 = const()[name = tensor("add_79_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(472733568)))]; + tensor add_79_gamma_0_to_fp16 = const()[name = tensor("add_79_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(472737472)))]; + tensor add_79_beta_0_to_fp16 = const()[name = tensor("add_79_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(472741376)))]; + tensor add_79_epsilon_0_to_fp16 = const()[name = tensor("add_79_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_79_cast_fp16 = batch_norm(beta = add_79_beta_0_to_fp16, epsilon = add_79_epsilon_0_to_fp16, gamma = add_79_gamma_0_to_fp16, mean = add_79_mean_0_to_fp16, variance = add_79_variance_0_to_fp16, x = reshape_157_cast_fp16)[name = tensor("add_79_cast_fp16")]; + tensor input_341_cast_fp16 = silu(x = add_79_cast_fp16)[name = tensor("input_341_cast_fp16")]; + tensor var_2853 = const()[name = tensor("op_2853"), val = tensor([1, 1])]; + tensor var_2855 = const()[name = tensor("op_2855"), val = tensor([1, 1])]; + tensor hidden_states_199_pad_type_0 = const()[name = tensor("hidden_states_199_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_199_pad_0 = const()[name = tensor("hidden_states_199_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(472745280)))]; + tensor up_blocks_1_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(516982144)))]; + tensor hidden_states_199_cast_fp16 = conv(bias = up_blocks_1_resnets_2_conv1_bias_to_fp16, dilations = var_2855, groups = var_2306, pad = hidden_states_199_pad_0, pad_type = hidden_states_199_pad_type_0, strides = var_2853, weight = up_blocks_1_resnets_2_conv1_weight_to_fp16, x = input_341_cast_fp16)[name = tensor("hidden_states_199_cast_fp16")]; + tensor var_2861 = const()[name = tensor("op_2861"), val = tensor([1, 1])]; + tensor var_2863 = const()[name = tensor("op_2863"), val = tensor([1, 1])]; + tensor temb_31_pad_type_0 = const()[name = tensor("temb_31_pad_type_0"), val = tensor("custom")]; + tensor temb_31_pad_0 = const()[name = tensor("temb_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(516984768)))]; + tensor up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(520261632)))]; + tensor temb_31_cast_fp16 = conv(bias = up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_2863, groups = var_2306, pad = temb_31_pad_0, pad_type = temb_31_pad_type_0, strides = var_2861, weight = up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16, x = cast_12)[name = tensor("temb_31_cast_fp16")]; + tensor input_345_cast_fp16 = add(x = hidden_states_199_cast_fp16, y = temb_31_cast_fp16)[name = tensor("input_345_cast_fp16")]; + tensor reshape_160_shape_0 = const()[name = tensor("reshape_160_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_160_cast_fp16 = reshape(shape = reshape_160_shape_0, x = input_345_cast_fp16)[name = tensor("reshape_160_cast_fp16")]; + tensor reduce_mean_120_axes_0 = const()[name = tensor("reduce_mean_120_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_120_keep_dims_0 = const()[name = tensor("reduce_mean_120_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_120_cast_fp16 = reduce_mean(axes = reduce_mean_120_axes_0, keep_dims = reduce_mean_120_keep_dims_0, x = reshape_160_cast_fp16)[name = tensor("reduce_mean_120_cast_fp16")]; + tensor sub_80_cast_fp16 = sub(x = reshape_160_cast_fp16, y = reduce_mean_120_cast_fp16)[name = tensor("sub_80_cast_fp16")]; + tensor square_40_cast_fp16 = square(x = sub_80_cast_fp16)[name = tensor("square_40_cast_fp16")]; + tensor reduce_mean_122_axes_0 = const()[name = tensor("reduce_mean_122_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_122_keep_dims_0 = const()[name = tensor("reduce_mean_122_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_122_cast_fp16 = reduce_mean(axes = reduce_mean_122_axes_0, keep_dims = reduce_mean_122_keep_dims_0, x = square_40_cast_fp16)[name = tensor("reduce_mean_122_cast_fp16")]; + tensor add_80_y_0_to_fp16 = const()[name = tensor("add_80_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_80_cast_fp16 = add(x = reduce_mean_122_cast_fp16, y = add_80_y_0_to_fp16)[name = tensor("add_80_cast_fp16")]; + tensor sqrt_40_cast_fp16 = sqrt(x = add_80_cast_fp16)[name = tensor("sqrt_40_cast_fp16")]; + tensor real_div_40_cast_fp16 = real_div(x = sub_80_cast_fp16, y = sqrt_40_cast_fp16)[name = tensor("real_div_40_cast_fp16")]; + tensor reshape_161_shape_0 = const()[name = tensor("reshape_161_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_161_cast_fp16 = reshape(shape = reshape_161_shape_0, x = real_div_40_cast_fp16)[name = tensor("reshape_161_cast_fp16")]; + tensor add_81_gamma_0_to_fp16 = const()[name = tensor("add_81_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(520264256)))]; + tensor add_81_beta_0_to_fp16 = const()[name = tensor("add_81_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(520266880)))]; + tensor add_81_epsilon_0_to_fp16 = const()[name = tensor("add_81_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_81_cast_fp16 = batch_norm(beta = add_81_beta_0_to_fp16, epsilon = add_81_epsilon_0_to_fp16, gamma = add_81_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_161_cast_fp16)[name = tensor("add_81_cast_fp16")]; + tensor input_349_cast_fp16 = silu(x = add_81_cast_fp16)[name = tensor("input_349_cast_fp16")]; + tensor var_2873 = const()[name = tensor("op_2873"), val = tensor([1, 1])]; + tensor var_2875 = const()[name = tensor("op_2875"), val = tensor([1, 1])]; + tensor hidden_states_201_pad_type_0 = const()[name = tensor("hidden_states_201_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_201_pad_0 = const()[name = tensor("hidden_states_201_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(520269504)))]; + tensor up_blocks_1_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(549760768)))]; + tensor hidden_states_201_cast_fp16 = conv(bias = up_blocks_1_resnets_2_conv2_bias_to_fp16, dilations = var_2875, groups = var_2306, pad = hidden_states_201_pad_0, pad_type = hidden_states_201_pad_type_0, strides = var_2873, weight = up_blocks_1_resnets_2_conv2_weight_to_fp16, x = input_349_cast_fp16)[name = tensor("hidden_states_201_cast_fp16")]; + tensor var_2880 = const()[name = tensor("op_2880"), val = tensor([1, 1])]; + tensor var_2882 = const()[name = tensor("op_2882"), val = tensor([1, 1])]; + tensor x_15_pad_type_0 = const()[name = tensor("x_15_pad_type_0"), val = tensor("custom")]; + tensor x_15_pad_0 = const()[name = tensor("x_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(549763392)))]; + tensor up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(554678656)))]; + tensor x_15_cast_fp16 = conv(bias = up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_2882, groups = var_2306, pad = x_15_pad_0, pad_type = x_15_pad_type_0, strides = var_2880, weight = up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16, x = input_337_cast_fp16)[name = tensor("x_15_cast_fp16")]; + tensor hidden_states_203_cast_fp16 = add(x = x_15_cast_fp16, y = hidden_states_201_cast_fp16)[name = tensor("hidden_states_203_cast_fp16")]; + tensor reshape_164_shape_0 = const()[name = tensor("reshape_164_shape_0"), val = tensor([2, 32, 40, 12, 20])]; + tensor reshape_164_cast_fp16 = reshape(shape = reshape_164_shape_0, x = hidden_states_203_cast_fp16)[name = tensor("reshape_164_cast_fp16")]; + tensor reduce_mean_123_axes_0 = const()[name = tensor("reduce_mean_123_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_123_keep_dims_0 = const()[name = tensor("reduce_mean_123_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_123_cast_fp16 = reduce_mean(axes = reduce_mean_123_axes_0, keep_dims = reduce_mean_123_keep_dims_0, x = reshape_164_cast_fp16)[name = tensor("reduce_mean_123_cast_fp16")]; + tensor sub_82_cast_fp16 = sub(x = reshape_164_cast_fp16, y = reduce_mean_123_cast_fp16)[name = tensor("sub_82_cast_fp16")]; + tensor square_41_cast_fp16 = square(x = sub_82_cast_fp16)[name = tensor("square_41_cast_fp16")]; + tensor reduce_mean_125_axes_0 = const()[name = tensor("reduce_mean_125_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_125_keep_dims_0 = const()[name = tensor("reduce_mean_125_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_125_cast_fp16 = reduce_mean(axes = reduce_mean_125_axes_0, keep_dims = reduce_mean_125_keep_dims_0, x = square_41_cast_fp16)[name = tensor("reduce_mean_125_cast_fp16")]; + tensor add_82_y_0_to_fp16 = const()[name = tensor("add_82_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_82_cast_fp16 = add(x = reduce_mean_125_cast_fp16, y = add_82_y_0_to_fp16)[name = tensor("add_82_cast_fp16")]; + tensor sqrt_41_cast_fp16 = sqrt(x = add_82_cast_fp16)[name = tensor("sqrt_41_cast_fp16")]; + tensor real_div_41_cast_fp16 = real_div(x = sub_82_cast_fp16, y = sqrt_41_cast_fp16)[name = tensor("real_div_41_cast_fp16")]; + tensor reshape_165_shape_0 = const()[name = tensor("reshape_165_shape_0"), val = tensor([2, 1280, 12, 20])]; + tensor reshape_165_cast_fp16 = reshape(shape = reshape_165_shape_0, x = real_div_41_cast_fp16)[name = tensor("reshape_165_cast_fp16")]; + tensor add_83_gamma_0_to_fp16 = const()[name = tensor("add_83_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(554681280)))]; + tensor add_83_beta_0_to_fp16 = const()[name = tensor("add_83_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(554683904)))]; + tensor add_83_epsilon_0_to_fp16 = const()[name = tensor("add_83_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_83_cast_fp16 = batch_norm(beta = add_83_beta_0_to_fp16, epsilon = add_83_epsilon_0_to_fp16, gamma = add_83_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_165_cast_fp16)[name = tensor("add_83_cast_fp16")]; + tensor var_2902 = const()[name = tensor("op_2902"), val = tensor([1, 1])]; + tensor var_2904 = const()[name = tensor("op_2904"), val = tensor([1, 1])]; + tensor hidden_states_205_pad_type_0 = const()[name = tensor("hidden_states_205_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_205_pad_0 = const()[name = tensor("hidden_states_205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(554686528)))]; + tensor up_blocks_1_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(557963392)))]; + tensor hidden_states_205_cast_fp16 = conv(bias = up_blocks_1_attentions_2_proj_in_bias_to_fp16, dilations = var_2904, groups = var_2306, pad = hidden_states_205_pad_0, pad_type = hidden_states_205_pad_type_0, strides = var_2902, weight = up_blocks_1_attentions_2_proj_in_weight_to_fp16, x = add_83_cast_fp16)[name = tensor("hidden_states_205_cast_fp16")]; + tensor var_2909 = const()[name = tensor("op_2909"), val = tensor([2, 1280, 1, 240])]; + tensor inputs_55_cast_fp16 = reshape(shape = var_2909, x = hidden_states_205_cast_fp16)[name = tensor("inputs_55_cast_fp16")]; + tensor var_2919 = const()[name = tensor("op_2919"), val = tensor([1])]; + tensor channels_mean_55_cast_fp16 = reduce_mean(axes = var_2919, keep_dims = var_2301, x = inputs_55_cast_fp16)[name = tensor("channels_mean_55_cast_fp16")]; + tensor zero_mean_55_cast_fp16 = sub(x = inputs_55_cast_fp16, y = channels_mean_55_cast_fp16)[name = tensor("zero_mean_55_cast_fp16")]; + tensor zero_mean_sq_55_cast_fp16 = mul(x = zero_mean_55_cast_fp16, y = zero_mean_55_cast_fp16)[name = tensor("zero_mean_sq_55_cast_fp16")]; + tensor var_2923 = const()[name = tensor("op_2923"), val = tensor([1])]; + tensor var_2924_cast_fp16 = reduce_mean(axes = var_2923, keep_dims = var_2301, x = zero_mean_sq_55_cast_fp16)[name = tensor("op_2924_cast_fp16")]; + tensor var_2925_to_fp16 = const()[name = tensor("op_2925_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2926_cast_fp16 = add(x = var_2924_cast_fp16, y = var_2925_to_fp16)[name = tensor("op_2926_cast_fp16")]; + tensor denom_55_epsilon_0_to_fp16 = const()[name = tensor("denom_55_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_55_cast_fp16 = rsqrt(epsilon = denom_55_epsilon_0_to_fp16, x = var_2926_cast_fp16)[name = tensor("denom_55_cast_fp16")]; + tensor out_55_cast_fp16 = mul(x = zero_mean_55_cast_fp16, y = denom_55_cast_fp16)[name = tensor("out_55_cast_fp16")]; + tensor var_2930_to_fp16 = const()[name = tensor("op_2930_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(557966016)))]; + tensor var_2931_cast_fp16 = add(x = out_55_cast_fp16, y = var_2930_to_fp16)[name = tensor("op_2931_cast_fp16")]; + tensor var_2933_to_fp16 = const()[name = tensor("op_2933_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(557968640)))]; + tensor hidden_states_207_cast_fp16 = mul(x = var_2931_cast_fp16, y = var_2933_to_fp16)[name = tensor("hidden_states_207_cast_fp16")]; + tensor var_2940 = const()[name = tensor("op_2940"), val = tensor([1, 1])]; + tensor var_2942 = const()[name = tensor("op_2942"), val = tensor([1, 1])]; + tensor q_37_pad_type_0 = const()[name = tensor("q_37_pad_type_0"), val = tensor("custom")]; + tensor q_37_pad_0 = const()[name = tensor("q_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(557971264)))]; + tensor q_37_cast_fp16 = conv(dilations = var_2942, groups = var_2306, pad = q_37_pad_0, pad_type = q_37_pad_type_0, strides = var_2940, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_207_cast_fp16)[name = tensor("q_37_cast_fp16")]; + tensor var_2946 = const()[name = tensor("op_2946"), val = tensor([1, 1])]; + tensor var_2948 = const()[name = tensor("op_2948"), val = tensor([1, 1])]; + tensor k_37_pad_type_0 = const()[name = tensor("k_37_pad_type_0"), val = tensor("custom")]; + tensor k_37_pad_0 = const()[name = tensor("k_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(561248128)))]; + tensor k_37_cast_fp16 = conv(dilations = var_2948, groups = var_2306, pad = k_37_pad_0, pad_type = k_37_pad_type_0, strides = var_2946, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_207_cast_fp16)[name = tensor("k_37_cast_fp16")]; + tensor var_2952 = const()[name = tensor("op_2952"), val = tensor([1, 1])]; + tensor var_2954 = const()[name = tensor("op_2954"), val = tensor([1, 1])]; + tensor v_37_pad_type_0 = const()[name = tensor("v_37_pad_type_0"), val = tensor("custom")]; + tensor v_37_pad_0 = const()[name = tensor("v_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(564524992)))]; + tensor v_37_cast_fp16 = conv(dilations = var_2954, groups = var_2306, pad = v_37_pad_0, pad_type = v_37_pad_type_0, strides = var_2952, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_207_cast_fp16)[name = tensor("v_37_cast_fp16")]; + tensor var_2958 = const()[name = tensor("op_2958"), val = tensor([2, 20, 64, -1])]; + tensor var_2959_cast_fp16 = reshape(shape = var_2958, x = q_37_cast_fp16)[name = tensor("op_2959_cast_fp16")]; + tensor var_2960 = const()[name = tensor("op_2960"), val = tensor([2, 20, 64, -1])]; + tensor var_2961_cast_fp16 = reshape(shape = var_2960, x = k_37_cast_fp16)[name = tensor("op_2961_cast_fp16")]; + tensor var_2962 = const()[name = tensor("op_2962"), val = tensor([2, 20, 64, -1])]; + tensor var_2963_cast_fp16 = reshape(shape = var_2962, x = v_37_cast_fp16)[name = tensor("op_2963_cast_fp16")]; + tensor attn_weights_73_transpose_x_0 = const()[name = tensor("attn_weights_73_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_73_transpose_y_0 = const()[name = tensor("attn_weights_73_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_73_cast_fp16 = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = var_2959_cast_fp16, y = var_2961_cast_fp16)[name = tensor("attn_weights_73_cast_fp16")]; + tensor attn_weights_75_cast_fp16 = mul(x = attn_weights_73_cast_fp16, y = var_2297_to_fp16)[name = tensor("attn_weights_75_cast_fp16")]; + tensor var_2967_cast_fp16 = softmax(axis = var_2290, x = attn_weights_75_cast_fp16)[name = tensor("op_2967_cast_fp16")]; + tensor attn_37_transpose_x_0 = const()[name = tensor("attn_37_transpose_x_0"), val = tensor(false)]; + tensor attn_37_transpose_y_0 = const()[name = tensor("attn_37_transpose_y_0"), val = tensor(true)]; + tensor attn_37_cast_fp16 = matmul(transpose_x = attn_37_transpose_x_0, transpose_y = attn_37_transpose_y_0, x = var_2963_cast_fp16, y = var_2967_cast_fp16)[name = tensor("attn_37_cast_fp16")]; + tensor var_2971 = const()[name = tensor("op_2971"), val = tensor([2, 1280, 1, -1])]; + tensor input_353_cast_fp16 = reshape(shape = var_2971, x = attn_37_cast_fp16)[name = tensor("input_353_cast_fp16")]; + tensor var_2976 = const()[name = tensor("op_2976"), val = tensor([1, 1])]; + tensor var_2978 = const()[name = tensor("op_2978"), val = tensor([1, 1])]; + tensor var_2980_pad_type_0 = const()[name = tensor("op_2980_pad_type_0"), val = tensor("custom")]; + tensor var_2980_pad_0 = const()[name = tensor("op_2980_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(567801856)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(571078720)))]; + tensor var_2980_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_2978, groups = var_2306, pad = var_2980_pad_0, pad_type = var_2980_pad_type_0, strides = var_2976, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_353_cast_fp16)[name = tensor("op_2980_cast_fp16")]; + tensor inputs_57_cast_fp16 = add(x = var_2980_cast_fp16, y = inputs_55_cast_fp16)[name = tensor("inputs_57_cast_fp16")]; + tensor var_2984 = const()[name = tensor("op_2984"), val = tensor([1])]; + tensor channels_mean_57_cast_fp16 = reduce_mean(axes = var_2984, keep_dims = var_2301, x = inputs_57_cast_fp16)[name = tensor("channels_mean_57_cast_fp16")]; + tensor zero_mean_57_cast_fp16 = sub(x = inputs_57_cast_fp16, y = channels_mean_57_cast_fp16)[name = tensor("zero_mean_57_cast_fp16")]; + tensor zero_mean_sq_57_cast_fp16 = mul(x = zero_mean_57_cast_fp16, y = zero_mean_57_cast_fp16)[name = tensor("zero_mean_sq_57_cast_fp16")]; + tensor var_2988 = const()[name = tensor("op_2988"), val = tensor([1])]; + tensor var_2989_cast_fp16 = reduce_mean(axes = var_2988, keep_dims = var_2301, x = zero_mean_sq_57_cast_fp16)[name = tensor("op_2989_cast_fp16")]; + tensor var_2990_to_fp16 = const()[name = tensor("op_2990_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2991_cast_fp16 = add(x = var_2989_cast_fp16, y = var_2990_to_fp16)[name = tensor("op_2991_cast_fp16")]; + tensor denom_57_epsilon_0_to_fp16 = const()[name = tensor("denom_57_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_57_cast_fp16 = rsqrt(epsilon = denom_57_epsilon_0_to_fp16, x = var_2991_cast_fp16)[name = tensor("denom_57_cast_fp16")]; + tensor out_57_cast_fp16 = mul(x = zero_mean_57_cast_fp16, y = denom_57_cast_fp16)[name = tensor("out_57_cast_fp16")]; + tensor var_2995_to_fp16 = const()[name = tensor("op_2995_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(571081344)))]; + tensor var_2996_cast_fp16 = add(x = out_57_cast_fp16, y = var_2995_to_fp16)[name = tensor("op_2996_cast_fp16")]; + tensor var_2998_to_fp16 = const()[name = tensor("op_2998_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(571083968)))]; + tensor hidden_states_209_cast_fp16 = mul(x = var_2996_cast_fp16, y = var_2998_to_fp16)[name = tensor("hidden_states_209_cast_fp16")]; + tensor var_3005 = const()[name = tensor("op_3005"), val = tensor([1, 1])]; + tensor var_3007 = const()[name = tensor("op_3007"), val = tensor([1, 1])]; + tensor q_39_pad_type_0 = const()[name = tensor("q_39_pad_type_0"), val = tensor("custom")]; + tensor q_39_pad_0 = const()[name = tensor("q_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(571086592)))]; + tensor q_39_cast_fp16 = conv(dilations = var_3007, groups = var_2306, pad = q_39_pad_0, pad_type = q_39_pad_type_0, strides = var_3005, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_209_cast_fp16)[name = tensor("q_39_cast_fp16")]; + tensor var_3011 = const()[name = tensor("op_3011"), val = tensor([1, 1])]; + tensor var_3013 = const()[name = tensor("op_3013"), val = tensor([1, 1])]; + tensor k_39_pad_type_0 = const()[name = tensor("k_39_pad_type_0"), val = tensor("custom")]; + tensor k_39_pad_0 = const()[name = tensor("k_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(574363456)))]; + tensor k_39_cast_fp16 = conv(dilations = var_3013, groups = var_2306, pad = k_39_pad_0, pad_type = k_39_pad_type_0, strides = var_3011, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_39_cast_fp16")]; + tensor var_3017 = const()[name = tensor("op_3017"), val = tensor([1, 1])]; + tensor var_3019 = const()[name = tensor("op_3019"), val = tensor([1, 1])]; + tensor v_39_pad_type_0 = const()[name = tensor("v_39_pad_type_0"), val = tensor("custom")]; + tensor v_39_pad_0 = const()[name = tensor("v_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(576984960)))]; + tensor v_39_cast_fp16 = conv(dilations = var_3019, groups = var_2306, pad = v_39_pad_0, pad_type = v_39_pad_type_0, strides = var_3017, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_39_cast_fp16")]; + tensor var_3023 = const()[name = tensor("op_3023"), val = tensor([2, 20, 64, -1])]; + tensor var_3024_cast_fp16 = reshape(shape = var_3023, x = q_39_cast_fp16)[name = tensor("op_3024_cast_fp16")]; + tensor var_3025 = const()[name = tensor("op_3025"), val = tensor([2, 20, 64, -1])]; + tensor var_3026_cast_fp16 = reshape(shape = var_3025, x = k_39_cast_fp16)[name = tensor("op_3026_cast_fp16")]; + tensor var_3027 = const()[name = tensor("op_3027"), val = tensor([2, 20, 64, -1])]; + tensor var_3028_cast_fp16 = reshape(shape = var_3027, x = v_39_cast_fp16)[name = tensor("op_3028_cast_fp16")]; + tensor attn_weights_77_transpose_x_0 = const()[name = tensor("attn_weights_77_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_77_transpose_y_0 = const()[name = tensor("attn_weights_77_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_77_cast_fp16 = matmul(transpose_x = attn_weights_77_transpose_x_0, transpose_y = attn_weights_77_transpose_y_0, x = var_3024_cast_fp16, y = var_3026_cast_fp16)[name = tensor("attn_weights_77_cast_fp16")]; + tensor attn_weights_79_cast_fp16 = mul(x = attn_weights_77_cast_fp16, y = var_2297_to_fp16)[name = tensor("attn_weights_79_cast_fp16")]; + tensor var_3032_cast_fp16 = softmax(axis = var_2290, x = attn_weights_79_cast_fp16)[name = tensor("op_3032_cast_fp16")]; + tensor attn_39_transpose_x_0 = const()[name = tensor("attn_39_transpose_x_0"), val = tensor(false)]; + tensor attn_39_transpose_y_0 = const()[name = tensor("attn_39_transpose_y_0"), val = tensor(true)]; + tensor attn_39_cast_fp16 = matmul(transpose_x = attn_39_transpose_x_0, transpose_y = attn_39_transpose_y_0, x = var_3028_cast_fp16, y = var_3032_cast_fp16)[name = tensor("attn_39_cast_fp16")]; + tensor var_3036 = const()[name = tensor("op_3036"), val = tensor([2, 1280, 1, -1])]; + tensor input_355_cast_fp16 = reshape(shape = var_3036, x = attn_39_cast_fp16)[name = tensor("input_355_cast_fp16")]; + tensor var_3041 = const()[name = tensor("op_3041"), val = tensor([1, 1])]; + tensor var_3043 = const()[name = tensor("op_3043"), val = tensor([1, 1])]; + tensor var_3045_pad_type_0 = const()[name = tensor("op_3045_pad_type_0"), val = tensor("custom")]; + tensor var_3045_pad_0 = const()[name = tensor("op_3045_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(579606464)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(582883328)))]; + tensor var_3045_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3043, groups = var_2306, pad = var_3045_pad_0, pad_type = var_3045_pad_type_0, strides = var_3041, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_355_cast_fp16)[name = tensor("op_3045_cast_fp16")]; + tensor inputs_59_cast_fp16 = add(x = var_3045_cast_fp16, y = inputs_57_cast_fp16)[name = tensor("inputs_59_cast_fp16")]; + tensor var_3049 = const()[name = tensor("op_3049"), val = tensor([1])]; + tensor channels_mean_59_cast_fp16 = reduce_mean(axes = var_3049, keep_dims = var_2301, x = inputs_59_cast_fp16)[name = tensor("channels_mean_59_cast_fp16")]; + tensor zero_mean_59_cast_fp16 = sub(x = inputs_59_cast_fp16, y = channels_mean_59_cast_fp16)[name = tensor("zero_mean_59_cast_fp16")]; + tensor zero_mean_sq_59_cast_fp16 = mul(x = zero_mean_59_cast_fp16, y = zero_mean_59_cast_fp16)[name = tensor("zero_mean_sq_59_cast_fp16")]; + tensor var_3053 = const()[name = tensor("op_3053"), val = tensor([1])]; + tensor var_3054_cast_fp16 = reduce_mean(axes = var_3053, keep_dims = var_2301, x = zero_mean_sq_59_cast_fp16)[name = tensor("op_3054_cast_fp16")]; + tensor var_3055_to_fp16 = const()[name = tensor("op_3055_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3056_cast_fp16 = add(x = var_3054_cast_fp16, y = var_3055_to_fp16)[name = tensor("op_3056_cast_fp16")]; + tensor denom_59_epsilon_0_to_fp16 = const()[name = tensor("denom_59_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_59_cast_fp16 = rsqrt(epsilon = denom_59_epsilon_0_to_fp16, x = var_3056_cast_fp16)[name = tensor("denom_59_cast_fp16")]; + tensor out_59_cast_fp16 = mul(x = zero_mean_59_cast_fp16, y = denom_59_cast_fp16)[name = tensor("out_59_cast_fp16")]; + tensor var_3060_to_fp16 = const()[name = tensor("op_3060_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(582885952)))]; + tensor var_3061_cast_fp16 = add(x = out_59_cast_fp16, y = var_3060_to_fp16)[name = tensor("op_3061_cast_fp16")]; + tensor var_3063_to_fp16 = const()[name = tensor("op_3063_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(582888576)))]; + tensor input_357_cast_fp16 = mul(x = var_3061_cast_fp16, y = var_3063_to_fp16)[name = tensor("input_357_cast_fp16")]; + tensor var_3071 = const()[name = tensor("op_3071"), val = tensor([1, 1])]; + tensor var_3073 = const()[name = tensor("op_3073"), val = tensor([1, 1])]; + tensor var_3075_pad_type_0 = const()[name = tensor("op_3075_pad_type_0"), val = tensor("custom")]; + tensor var_3075_pad_0 = const()[name = tensor("op_3075_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(582891200)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(609105664)))]; + tensor var_3075_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_3073, groups = var_2306, pad = var_3075_pad_0, pad_type = var_3075_pad_type_0, strides = var_3071, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_357_cast_fp16)[name = tensor("op_3075_cast_fp16")]; + tensor var_3076_split_sizes_0 = const()[name = tensor("op_3076_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3076_axis_0 = const()[name = tensor("op_3076_axis_0"), val = tensor(1)]; + tensor var_3076_cast_fp16_0, tensor var_3076_cast_fp16_1 = split(axis = var_3076_axis_0, split_sizes = var_3076_split_sizes_0, x = var_3075_cast_fp16)[name = tensor("op_3076_cast_fp16")]; + tensor var_3078_mode_0 = const()[name = tensor("op_3078_mode_0"), val = tensor("EXACT")]; + tensor var_3078_cast_fp16 = gelu(mode = var_3078_mode_0, x = var_3076_cast_fp16_1)[name = tensor("op_3078_cast_fp16")]; + tensor input_359_cast_fp16 = mul(x = var_3076_cast_fp16_0, y = var_3078_cast_fp16)[name = tensor("input_359_cast_fp16")]; + tensor var_3082 = const()[name = tensor("op_3082"), val = tensor([1, 1])]; + tensor var_3084 = const()[name = tensor("op_3084"), val = tensor([1, 1])]; + tensor var_3086_pad_type_0 = const()[name = tensor("op_3086_pad_type_0"), val = tensor("custom")]; + tensor var_3086_pad_0 = const()[name = tensor("op_3086_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(609126208)))]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622233472)))]; + tensor var_3086_cast_fp16 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3084, groups = var_2306, pad = var_3086_pad_0, pad_type = var_3086_pad_type_0, strides = var_3082, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_359_cast_fp16)[name = tensor("op_3086_cast_fp16")]; + tensor hidden_states_213_cast_fp16 = add(x = var_3086_cast_fp16, y = inputs_59_cast_fp16)[name = tensor("hidden_states_213_cast_fp16")]; + tensor var_3088 = const()[name = tensor("op_3088"), val = tensor([2, 1280, 12, 20])]; + tensor input_361_cast_fp16 = reshape(shape = var_3088, x = hidden_states_213_cast_fp16)[name = tensor("input_361_cast_fp16")]; + tensor var_3092 = const()[name = tensor("op_3092"), val = tensor([1, 1])]; + tensor var_3094 = const()[name = tensor("op_3094"), val = tensor([1, 1])]; + tensor hidden_states_215_pad_type_0 = const()[name = tensor("hidden_states_215_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_215_pad_0 = const()[name = tensor("hidden_states_215_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622236096)))]; + tensor up_blocks_1_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(625512960)))]; + tensor hidden_states_215_cast_fp16 = conv(bias = up_blocks_1_attentions_2_proj_out_bias_to_fp16, dilations = var_3094, groups = var_2306, pad = hidden_states_215_pad_0, pad_type = hidden_states_215_pad_type_0, strides = var_3092, weight = up_blocks_1_attentions_2_proj_out_weight_to_fp16, x = input_361_cast_fp16)[name = tensor("hidden_states_215_cast_fp16")]; + tensor input_363_cast_fp16 = add(x = hidden_states_215_cast_fp16, y = hidden_states_203_cast_fp16)[name = tensor("input_363_cast_fp16")]; + tensor input_365_scale_factor_height_0 = const()[name = tensor("input_365_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_365_scale_factor_width_0 = const()[name = tensor("input_365_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_365_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_365_scale_factor_height_0, scale_factor_width = input_365_scale_factor_width_0, x = input_363_cast_fp16)[name = tensor("input_365_cast_fp16")]; + tensor var_3103 = const()[name = tensor("op_3103"), val = tensor([1, 1])]; + tensor var_3105 = const()[name = tensor("op_3105"), val = tensor([1, 1])]; + tensor hidden_states_217_pad_type_0 = const()[name = tensor("hidden_states_217_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_217_pad_0 = const()[name = tensor("hidden_states_217_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("up_blocks_1_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(625515584)))]; + tensor up_blocks_1_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_1_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655006848)))]; + tensor hidden_states_217_cast_fp16 = conv(bias = up_blocks_1_upsamplers_0_conv_bias_to_fp16, dilations = var_3105, groups = var_2306, pad = hidden_states_217_pad_0, pad_type = hidden_states_217_pad_type_0, strides = var_3103, weight = up_blocks_1_upsamplers_0_conv_weight_to_fp16, x = input_365_cast_fp16)[name = tensor("hidden_states_217_cast_fp16")]; + tensor var_3110 = const()[name = tensor("op_3110"), val = tensor(3)]; + tensor var_3121 = const()[name = tensor("op_3121"), val = tensor(true)]; + tensor var_3126 = const()[name = tensor("op_3126"), val = tensor(1)]; + tensor input_367_interleave_0 = const()[name = tensor("input_367_interleave_0"), val = tensor(false)]; + tensor cast_7 = cast(dtype = cast_12_dtype_0, x = input_115_cast_fp16)[name = tensor("cast_7")]; + tensor input_367_cast_fp16 = concat(axis = var_3126, interleave = input_367_interleave_0, values = (hidden_states_217_cast_fp16, cast_7))[name = tensor("input_367_cast_fp16")]; + tensor reshape_168_shape_0 = const()[name = tensor("reshape_168_shape_0"), val = tensor([2, 32, 60, 24, 40])]; + tensor reshape_168_cast_fp16 = reshape(shape = reshape_168_shape_0, x = input_367_cast_fp16)[name = tensor("reshape_168_cast_fp16")]; + tensor reduce_mean_126_axes_0 = const()[name = tensor("reduce_mean_126_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_126_keep_dims_0 = const()[name = tensor("reduce_mean_126_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_126_cast_fp16 = reduce_mean(axes = reduce_mean_126_axes_0, keep_dims = reduce_mean_126_keep_dims_0, x = reshape_168_cast_fp16)[name = tensor("reduce_mean_126_cast_fp16")]; + tensor sub_84_cast_fp16 = sub(x = reshape_168_cast_fp16, y = reduce_mean_126_cast_fp16)[name = tensor("sub_84_cast_fp16")]; + tensor square_42_cast_fp16 = square(x = sub_84_cast_fp16)[name = tensor("square_42_cast_fp16")]; + tensor reduce_mean_128_axes_0 = const()[name = tensor("reduce_mean_128_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_128_keep_dims_0 = const()[name = tensor("reduce_mean_128_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_128_cast_fp16 = reduce_mean(axes = reduce_mean_128_axes_0, keep_dims = reduce_mean_128_keep_dims_0, x = square_42_cast_fp16)[name = tensor("reduce_mean_128_cast_fp16")]; + tensor add_84_y_0_to_fp16 = const()[name = tensor("add_84_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_84_cast_fp16 = add(x = reduce_mean_128_cast_fp16, y = add_84_y_0_to_fp16)[name = tensor("add_84_cast_fp16")]; + tensor sqrt_42_cast_fp16 = sqrt(x = add_84_cast_fp16)[name = tensor("sqrt_42_cast_fp16")]; + tensor real_div_42_cast_fp16 = real_div(x = sub_84_cast_fp16, y = sqrt_42_cast_fp16)[name = tensor("real_div_42_cast_fp16")]; + tensor reshape_169_shape_0 = const()[name = tensor("reshape_169_shape_0"), val = tensor([2, 1920, 24, 40])]; + tensor reshape_169_cast_fp16 = reshape(shape = reshape_169_shape_0, x = real_div_42_cast_fp16)[name = tensor("reshape_169_cast_fp16")]; + tensor add_85_gamma_0_to_fp16 = const()[name = tensor("add_85_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655009472)))]; + tensor add_85_beta_0_to_fp16 = const()[name = tensor("add_85_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655013376)))]; + tensor add_85_epsilon_0_to_fp16 = const()[name = tensor("add_85_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_85_cast_fp16 = batch_norm(beta = add_85_beta_0_to_fp16, epsilon = add_85_epsilon_0_to_fp16, gamma = add_85_gamma_0_to_fp16, mean = add_79_mean_0_to_fp16, variance = add_79_variance_0_to_fp16, x = reshape_169_cast_fp16)[name = tensor("add_85_cast_fp16")]; + tensor input_371_cast_fp16 = silu(x = add_85_cast_fp16)[name = tensor("input_371_cast_fp16")]; + tensor var_3155 = const()[name = tensor("op_3155"), val = tensor([1, 1])]; + tensor var_3157 = const()[name = tensor("op_3157"), val = tensor([1, 1])]; + tensor hidden_states_219_pad_type_0 = const()[name = tensor("hidden_states_219_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_219_pad_0 = const()[name = tensor("hidden_states_219_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(655017280)))]; + tensor up_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(677135744)))]; + tensor hidden_states_219_cast_fp16 = conv(bias = up_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_3157, groups = var_3126, pad = hidden_states_219_pad_0, pad_type = hidden_states_219_pad_type_0, strides = var_3155, weight = up_blocks_2_resnets_0_conv1_weight_to_fp16, x = input_371_cast_fp16)[name = tensor("hidden_states_219_cast_fp16")]; + tensor var_3163 = const()[name = tensor("op_3163"), val = tensor([1, 1])]; + tensor var_3165 = const()[name = tensor("op_3165"), val = tensor([1, 1])]; + tensor temb_33_pad_type_0 = const()[name = tensor("temb_33_pad_type_0"), val = tensor("custom")]; + tensor temb_33_pad_0 = const()[name = tensor("temb_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(677137088)))]; + tensor up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(678775552)))]; + tensor temb_33_cast_fp16 = conv(bias = up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_3165, groups = var_3126, pad = temb_33_pad_0, pad_type = temb_33_pad_type_0, strides = var_3163, weight = up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16, x = cast_12)[name = tensor("temb_33_cast_fp16")]; + tensor input_375_cast_fp16 = add(x = hidden_states_219_cast_fp16, y = temb_33_cast_fp16)[name = tensor("input_375_cast_fp16")]; + tensor reshape_172_shape_0 = const()[name = tensor("reshape_172_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_172_cast_fp16 = reshape(shape = reshape_172_shape_0, x = input_375_cast_fp16)[name = tensor("reshape_172_cast_fp16")]; + tensor reduce_mean_129_axes_0 = const()[name = tensor("reduce_mean_129_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_129_keep_dims_0 = const()[name = tensor("reduce_mean_129_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_129_cast_fp16 = reduce_mean(axes = reduce_mean_129_axes_0, keep_dims = reduce_mean_129_keep_dims_0, x = reshape_172_cast_fp16)[name = tensor("reduce_mean_129_cast_fp16")]; + tensor sub_86_cast_fp16 = sub(x = reshape_172_cast_fp16, y = reduce_mean_129_cast_fp16)[name = tensor("sub_86_cast_fp16")]; + tensor square_43_cast_fp16 = square(x = sub_86_cast_fp16)[name = tensor("square_43_cast_fp16")]; + tensor reduce_mean_131_axes_0 = const()[name = tensor("reduce_mean_131_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_131_keep_dims_0 = const()[name = tensor("reduce_mean_131_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_131_cast_fp16 = reduce_mean(axes = reduce_mean_131_axes_0, keep_dims = reduce_mean_131_keep_dims_0, x = square_43_cast_fp16)[name = tensor("reduce_mean_131_cast_fp16")]; + tensor add_86_y_0_to_fp16 = const()[name = tensor("add_86_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_86_cast_fp16 = add(x = reduce_mean_131_cast_fp16, y = add_86_y_0_to_fp16)[name = tensor("add_86_cast_fp16")]; + tensor sqrt_43_cast_fp16 = sqrt(x = add_86_cast_fp16)[name = tensor("sqrt_43_cast_fp16")]; + tensor real_div_43_cast_fp16 = real_div(x = sub_86_cast_fp16, y = sqrt_43_cast_fp16)[name = tensor("real_div_43_cast_fp16")]; + tensor reshape_173_shape_0 = const()[name = tensor("reshape_173_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_173_cast_fp16 = reshape(shape = reshape_173_shape_0, x = real_div_43_cast_fp16)[name = tensor("reshape_173_cast_fp16")]; + tensor add_87_gamma_0_to_fp16 = const()[name = tensor("add_87_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(678776896)))]; + tensor add_87_beta_0_to_fp16 = const()[name = tensor("add_87_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(678778240)))]; + tensor add_87_epsilon_0_to_fp16 = const()[name = tensor("add_87_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_87_cast_fp16 = batch_norm(beta = add_87_beta_0_to_fp16, epsilon = add_87_epsilon_0_to_fp16, gamma = add_87_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_173_cast_fp16)[name = tensor("add_87_cast_fp16")]; + tensor input_379_cast_fp16 = silu(x = add_87_cast_fp16)[name = tensor("input_379_cast_fp16")]; + tensor var_3175 = const()[name = tensor("op_3175"), val = tensor([1, 1])]; + tensor var_3177 = const()[name = tensor("op_3177"), val = tensor([1, 1])]; + tensor hidden_states_221_pad_type_0 = const()[name = tensor("hidden_states_221_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_221_pad_0 = const()[name = tensor("hidden_states_221_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(678779584)))]; + tensor up_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(686152448)))]; + tensor hidden_states_221_cast_fp16 = conv(bias = up_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_3177, groups = var_3126, pad = hidden_states_221_pad_0, pad_type = hidden_states_221_pad_type_0, strides = var_3175, weight = up_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_379_cast_fp16)[name = tensor("hidden_states_221_cast_fp16")]; + tensor var_3182 = const()[name = tensor("op_3182"), val = tensor([1, 1])]; + tensor var_3184 = const()[name = tensor("op_3184"), val = tensor([1, 1])]; + tensor x_17_pad_type_0 = const()[name = tensor("x_17_pad_type_0"), val = tensor("custom")]; + tensor x_17_pad_0 = const()[name = tensor("x_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(686153792)))]; + tensor up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(688611456)))]; + tensor x_17_cast_fp16 = conv(bias = up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_3184, groups = var_3126, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = var_3182, weight = up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16, x = input_367_cast_fp16)[name = tensor("x_17_cast_fp16")]; + tensor hidden_states_223_cast_fp16 = add(x = x_17_cast_fp16, y = hidden_states_221_cast_fp16)[name = tensor("hidden_states_223_cast_fp16")]; + tensor reshape_176_shape_0 = const()[name = tensor("reshape_176_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_176_cast_fp16 = reshape(shape = reshape_176_shape_0, x = hidden_states_223_cast_fp16)[name = tensor("reshape_176_cast_fp16")]; + tensor reduce_mean_132_axes_0 = const()[name = tensor("reduce_mean_132_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_132_keep_dims_0 = const()[name = tensor("reduce_mean_132_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_132_cast_fp16 = reduce_mean(axes = reduce_mean_132_axes_0, keep_dims = reduce_mean_132_keep_dims_0, x = reshape_176_cast_fp16)[name = tensor("reduce_mean_132_cast_fp16")]; + tensor sub_88_cast_fp16 = sub(x = reshape_176_cast_fp16, y = reduce_mean_132_cast_fp16)[name = tensor("sub_88_cast_fp16")]; + tensor square_44_cast_fp16 = square(x = sub_88_cast_fp16)[name = tensor("square_44_cast_fp16")]; + tensor reduce_mean_134_axes_0 = const()[name = tensor("reduce_mean_134_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_134_keep_dims_0 = const()[name = tensor("reduce_mean_134_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_134_cast_fp16 = reduce_mean(axes = reduce_mean_134_axes_0, keep_dims = reduce_mean_134_keep_dims_0, x = square_44_cast_fp16)[name = tensor("reduce_mean_134_cast_fp16")]; + tensor add_88_y_0_to_fp16 = const()[name = tensor("add_88_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_88_cast_fp16 = add(x = reduce_mean_134_cast_fp16, y = add_88_y_0_to_fp16)[name = tensor("add_88_cast_fp16")]; + tensor sqrt_44_cast_fp16 = sqrt(x = add_88_cast_fp16)[name = tensor("sqrt_44_cast_fp16")]; + tensor real_div_44_cast_fp16 = real_div(x = sub_88_cast_fp16, y = sqrt_44_cast_fp16)[name = tensor("real_div_44_cast_fp16")]; + tensor reshape_177_shape_0 = const()[name = tensor("reshape_177_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_177_cast_fp16 = reshape(shape = reshape_177_shape_0, x = real_div_44_cast_fp16)[name = tensor("reshape_177_cast_fp16")]; + tensor add_89_gamma_0_to_fp16 = const()[name = tensor("add_89_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(688612800)))]; + tensor add_89_beta_0_to_fp16 = const()[name = tensor("add_89_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(688614144)))]; + tensor add_89_epsilon_0_to_fp16 = const()[name = tensor("add_89_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_89_cast_fp16 = batch_norm(beta = add_89_beta_0_to_fp16, epsilon = add_89_epsilon_0_to_fp16, gamma = add_89_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_177_cast_fp16)[name = tensor("add_89_cast_fp16")]; + tensor var_3204 = const()[name = tensor("op_3204"), val = tensor([1, 1])]; + tensor var_3206 = const()[name = tensor("op_3206"), val = tensor([1, 1])]; + tensor hidden_states_225_pad_type_0 = const()[name = tensor("hidden_states_225_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_225_pad_0 = const()[name = tensor("hidden_states_225_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(688615488)))]; + tensor up_blocks_2_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(689434752)))]; + tensor hidden_states_225_cast_fp16 = conv(bias = up_blocks_2_attentions_0_proj_in_bias_to_fp16, dilations = var_3206, groups = var_3126, pad = hidden_states_225_pad_0, pad_type = hidden_states_225_pad_type_0, strides = var_3204, weight = up_blocks_2_attentions_0_proj_in_weight_to_fp16, x = add_89_cast_fp16)[name = tensor("hidden_states_225_cast_fp16")]; + tensor var_3211 = const()[name = tensor("op_3211"), val = tensor([2, 640, 1, 960])]; + tensor inputs_61_cast_fp16 = reshape(shape = var_3211, x = hidden_states_225_cast_fp16)[name = tensor("inputs_61_cast_fp16")]; + tensor var_3221 = const()[name = tensor("op_3221"), val = tensor([1])]; + tensor channels_mean_61_cast_fp16 = reduce_mean(axes = var_3221, keep_dims = var_3121, x = inputs_61_cast_fp16)[name = tensor("channels_mean_61_cast_fp16")]; + tensor zero_mean_61_cast_fp16 = sub(x = inputs_61_cast_fp16, y = channels_mean_61_cast_fp16)[name = tensor("zero_mean_61_cast_fp16")]; + tensor zero_mean_sq_61_cast_fp16 = mul(x = zero_mean_61_cast_fp16, y = zero_mean_61_cast_fp16)[name = tensor("zero_mean_sq_61_cast_fp16")]; + tensor var_3225 = const()[name = tensor("op_3225"), val = tensor([1])]; + tensor var_3226_cast_fp16 = reduce_mean(axes = var_3225, keep_dims = var_3121, x = zero_mean_sq_61_cast_fp16)[name = tensor("op_3226_cast_fp16")]; + tensor var_3227_to_fp16 = const()[name = tensor("op_3227_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3228_cast_fp16 = add(x = var_3226_cast_fp16, y = var_3227_to_fp16)[name = tensor("op_3228_cast_fp16")]; + tensor denom_61_epsilon_0_to_fp16 = const()[name = tensor("denom_61_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_61_cast_fp16 = rsqrt(epsilon = denom_61_epsilon_0_to_fp16, x = var_3228_cast_fp16)[name = tensor("denom_61_cast_fp16")]; + tensor out_61_cast_fp16 = mul(x = zero_mean_61_cast_fp16, y = denom_61_cast_fp16)[name = tensor("out_61_cast_fp16")]; + tensor var_3232_to_fp16 = const()[name = tensor("op_3232_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(689436096)))]; + tensor var_3233_cast_fp16 = add(x = out_61_cast_fp16, y = var_3232_to_fp16)[name = tensor("op_3233_cast_fp16")]; + tensor var_3235_to_fp16 = const()[name = tensor("op_3235_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(689437440)))]; + tensor hidden_states_227_cast_fp16 = mul(x = var_3233_cast_fp16, y = var_3235_to_fp16)[name = tensor("hidden_states_227_cast_fp16")]; + tensor var_3242 = const()[name = tensor("op_3242"), val = tensor([1, 1])]; + tensor var_3244 = const()[name = tensor("op_3244"), val = tensor([1, 1])]; + tensor q_41_pad_type_0 = const()[name = tensor("q_41_pad_type_0"), val = tensor("custom")]; + tensor q_41_pad_0 = const()[name = tensor("q_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(689438784)))]; + tensor q_41_cast_fp16 = conv(dilations = var_3244, groups = var_3126, pad = q_41_pad_0, pad_type = q_41_pad_type_0, strides = var_3242, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_227_cast_fp16)[name = tensor("q_41_cast_fp16")]; + tensor var_3248 = const()[name = tensor("op_3248"), val = tensor([1, 1])]; + tensor var_3250 = const()[name = tensor("op_3250"), val = tensor([1, 1])]; + tensor k_41_pad_type_0 = const()[name = tensor("k_41_pad_type_0"), val = tensor("custom")]; + tensor k_41_pad_0 = const()[name = tensor("k_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(690258048)))]; + tensor k_41_cast_fp16 = conv(dilations = var_3250, groups = var_3126, pad = k_41_pad_0, pad_type = k_41_pad_type_0, strides = var_3248, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_227_cast_fp16)[name = tensor("k_41_cast_fp16")]; + tensor var_3254 = const()[name = tensor("op_3254"), val = tensor([1, 1])]; + tensor var_3256 = const()[name = tensor("op_3256"), val = tensor([1, 1])]; + tensor v_41_pad_type_0 = const()[name = tensor("v_41_pad_type_0"), val = tensor("custom")]; + tensor v_41_pad_0 = const()[name = tensor("v_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(691077312)))]; + tensor v_41_cast_fp16 = conv(dilations = var_3256, groups = var_3126, pad = v_41_pad_0, pad_type = v_41_pad_type_0, strides = var_3254, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_227_cast_fp16)[name = tensor("v_41_cast_fp16")]; + tensor var_3260 = const()[name = tensor("op_3260"), val = tensor([2, 10, 64, -1])]; + tensor var_3261_cast_fp16 = reshape(shape = var_3260, x = q_41_cast_fp16)[name = tensor("op_3261_cast_fp16")]; + tensor var_3262 = const()[name = tensor("op_3262"), val = tensor([2, 10, 64, -1])]; + tensor var_3263_cast_fp16 = reshape(shape = var_3262, x = k_41_cast_fp16)[name = tensor("op_3263_cast_fp16")]; + tensor var_3264 = const()[name = tensor("op_3264"), val = tensor([2, 10, 64, -1])]; + tensor var_3265_cast_fp16 = reshape(shape = var_3264, x = v_41_cast_fp16)[name = tensor("op_3265_cast_fp16")]; + tensor attn_weights_81_transpose_x_0 = const()[name = tensor("attn_weights_81_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_81_transpose_y_0 = const()[name = tensor("attn_weights_81_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_81_cast_fp16 = matmul(transpose_x = attn_weights_81_transpose_x_0, transpose_y = attn_weights_81_transpose_y_0, x = var_3261_cast_fp16, y = var_3263_cast_fp16)[name = tensor("attn_weights_81_cast_fp16")]; + tensor var_3117_to_fp16 = const()[name = tensor("op_3117_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_83_cast_fp16 = mul(x = attn_weights_81_cast_fp16, y = var_3117_to_fp16)[name = tensor("attn_weights_83_cast_fp16")]; + tensor var_3269_cast_fp16 = softmax(axis = var_3110, x = attn_weights_83_cast_fp16)[name = tensor("op_3269_cast_fp16")]; + tensor attn_41_transpose_x_0 = const()[name = tensor("attn_41_transpose_x_0"), val = tensor(false)]; + tensor attn_41_transpose_y_0 = const()[name = tensor("attn_41_transpose_y_0"), val = tensor(true)]; + tensor attn_41_cast_fp16 = matmul(transpose_x = attn_41_transpose_x_0, transpose_y = attn_41_transpose_y_0, x = var_3265_cast_fp16, y = var_3269_cast_fp16)[name = tensor("attn_41_cast_fp16")]; + tensor var_3273 = const()[name = tensor("op_3273"), val = tensor([2, 640, 1, -1])]; + tensor input_383_cast_fp16 = reshape(shape = var_3273, x = attn_41_cast_fp16)[name = tensor("input_383_cast_fp16")]; + tensor var_3278 = const()[name = tensor("op_3278"), val = tensor([1, 1])]; + tensor var_3280 = const()[name = tensor("op_3280"), val = tensor([1, 1])]; + tensor var_3282_pad_type_0 = const()[name = tensor("op_3282_pad_type_0"), val = tensor("custom")]; + tensor var_3282_pad_0 = const()[name = tensor("op_3282_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(691896576)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(692715840)))]; + tensor var_3282_cast_fp16 = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3280, groups = var_3126, pad = var_3282_pad_0, pad_type = var_3282_pad_type_0, strides = var_3278, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_383_cast_fp16)[name = tensor("op_3282_cast_fp16")]; + tensor inputs_63_cast_fp16 = add(x = var_3282_cast_fp16, y = inputs_61_cast_fp16)[name = tensor("inputs_63_cast_fp16")]; + tensor var_3286 = const()[name = tensor("op_3286"), val = tensor([1])]; + tensor channels_mean_63_cast_fp16 = reduce_mean(axes = var_3286, keep_dims = var_3121, x = inputs_63_cast_fp16)[name = tensor("channels_mean_63_cast_fp16")]; + tensor zero_mean_63_cast_fp16 = sub(x = inputs_63_cast_fp16, y = channels_mean_63_cast_fp16)[name = tensor("zero_mean_63_cast_fp16")]; + tensor zero_mean_sq_63_cast_fp16 = mul(x = zero_mean_63_cast_fp16, y = zero_mean_63_cast_fp16)[name = tensor("zero_mean_sq_63_cast_fp16")]; + tensor var_3290 = const()[name = tensor("op_3290"), val = tensor([1])]; + tensor var_3291_cast_fp16 = reduce_mean(axes = var_3290, keep_dims = var_3121, x = zero_mean_sq_63_cast_fp16)[name = tensor("op_3291_cast_fp16")]; + tensor var_3292_to_fp16 = const()[name = tensor("op_3292_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3293_cast_fp16 = add(x = var_3291_cast_fp16, y = var_3292_to_fp16)[name = tensor("op_3293_cast_fp16")]; + tensor denom_63_epsilon_0_to_fp16 = const()[name = tensor("denom_63_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_63_cast_fp16 = rsqrt(epsilon = denom_63_epsilon_0_to_fp16, x = var_3293_cast_fp16)[name = tensor("denom_63_cast_fp16")]; + tensor out_63_cast_fp16 = mul(x = zero_mean_63_cast_fp16, y = denom_63_cast_fp16)[name = tensor("out_63_cast_fp16")]; + tensor var_3297_to_fp16 = const()[name = tensor("op_3297_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(692717184)))]; + tensor var_3298_cast_fp16 = add(x = out_63_cast_fp16, y = var_3297_to_fp16)[name = tensor("op_3298_cast_fp16")]; + tensor var_3300_to_fp16 = const()[name = tensor("op_3300_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(692718528)))]; + tensor hidden_states_229_cast_fp16 = mul(x = var_3298_cast_fp16, y = var_3300_to_fp16)[name = tensor("hidden_states_229_cast_fp16")]; + tensor var_3307 = const()[name = tensor("op_3307"), val = tensor([1, 1])]; + tensor var_3309 = const()[name = tensor("op_3309"), val = tensor([1, 1])]; + tensor q_43_pad_type_0 = const()[name = tensor("q_43_pad_type_0"), val = tensor("custom")]; + tensor q_43_pad_0 = const()[name = tensor("q_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(692719872)))]; + tensor q_43_cast_fp16 = conv(dilations = var_3309, groups = var_3126, pad = q_43_pad_0, pad_type = q_43_pad_type_0, strides = var_3307, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_229_cast_fp16)[name = tensor("q_43_cast_fp16")]; + tensor var_3313 = const()[name = tensor("op_3313"), val = tensor([1, 1])]; + tensor var_3315 = const()[name = tensor("op_3315"), val = tensor([1, 1])]; + tensor k_43_pad_type_0 = const()[name = tensor("k_43_pad_type_0"), val = tensor("custom")]; + tensor k_43_pad_0 = const()[name = tensor("k_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(693539136)))]; + tensor k_43_cast_fp16 = conv(dilations = var_3315, groups = var_3126, pad = k_43_pad_0, pad_type = k_43_pad_type_0, strides = var_3313, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_43_cast_fp16")]; + tensor var_3319 = const()[name = tensor("op_3319"), val = tensor([1, 1])]; + tensor var_3321 = const()[name = tensor("op_3321"), val = tensor([1, 1])]; + tensor v_43_pad_type_0 = const()[name = tensor("v_43_pad_type_0"), val = tensor("custom")]; + tensor v_43_pad_0 = const()[name = tensor("v_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(694849920)))]; + tensor v_43_cast_fp16 = conv(dilations = var_3321, groups = var_3126, pad = v_43_pad_0, pad_type = v_43_pad_type_0, strides = var_3319, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_43_cast_fp16")]; + tensor var_3325 = const()[name = tensor("op_3325"), val = tensor([2, 10, 64, -1])]; + tensor var_3326_cast_fp16 = reshape(shape = var_3325, x = q_43_cast_fp16)[name = tensor("op_3326_cast_fp16")]; + tensor var_3327 = const()[name = tensor("op_3327"), val = tensor([2, 10, 64, -1])]; + tensor var_3328_cast_fp16 = reshape(shape = var_3327, x = k_43_cast_fp16)[name = tensor("op_3328_cast_fp16")]; + tensor var_3329 = const()[name = tensor("op_3329"), val = tensor([2, 10, 64, -1])]; + tensor var_3330_cast_fp16 = reshape(shape = var_3329, x = v_43_cast_fp16)[name = tensor("op_3330_cast_fp16")]; + tensor attn_weights_85_transpose_x_0 = const()[name = tensor("attn_weights_85_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_85_transpose_y_0 = const()[name = tensor("attn_weights_85_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_85_cast_fp16 = matmul(transpose_x = attn_weights_85_transpose_x_0, transpose_y = attn_weights_85_transpose_y_0, x = var_3326_cast_fp16, y = var_3328_cast_fp16)[name = tensor("attn_weights_85_cast_fp16")]; + tensor attn_weights_87_cast_fp16 = mul(x = attn_weights_85_cast_fp16, y = var_3117_to_fp16)[name = tensor("attn_weights_87_cast_fp16")]; + tensor var_3334_cast_fp16 = softmax(axis = var_3110, x = attn_weights_87_cast_fp16)[name = tensor("op_3334_cast_fp16")]; + tensor attn_43_transpose_x_0 = const()[name = tensor("attn_43_transpose_x_0"), val = tensor(false)]; + tensor attn_43_transpose_y_0 = const()[name = tensor("attn_43_transpose_y_0"), val = tensor(true)]; + tensor attn_43_cast_fp16 = matmul(transpose_x = attn_43_transpose_x_0, transpose_y = attn_43_transpose_y_0, x = var_3330_cast_fp16, y = var_3334_cast_fp16)[name = tensor("attn_43_cast_fp16")]; + tensor var_3338 = const()[name = tensor("op_3338"), val = tensor([2, 640, 1, -1])]; + tensor input_385_cast_fp16 = reshape(shape = var_3338, x = attn_43_cast_fp16)[name = tensor("input_385_cast_fp16")]; + tensor var_3343 = const()[name = tensor("op_3343"), val = tensor([1, 1])]; + tensor var_3345 = const()[name = tensor("op_3345"), val = tensor([1, 1])]; + tensor var_3347_pad_type_0 = const()[name = tensor("op_3347_pad_type_0"), val = tensor("custom")]; + tensor var_3347_pad_0 = const()[name = tensor("op_3347_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(696160704)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(696979968)))]; + tensor var_3347_cast_fp16 = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3345, groups = var_3126, pad = var_3347_pad_0, pad_type = var_3347_pad_type_0, strides = var_3343, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_385_cast_fp16)[name = tensor("op_3347_cast_fp16")]; + tensor inputs_65_cast_fp16 = add(x = var_3347_cast_fp16, y = inputs_63_cast_fp16)[name = tensor("inputs_65_cast_fp16")]; + tensor var_3351 = const()[name = tensor("op_3351"), val = tensor([1])]; + tensor channels_mean_65_cast_fp16 = reduce_mean(axes = var_3351, keep_dims = var_3121, x = inputs_65_cast_fp16)[name = tensor("channels_mean_65_cast_fp16")]; + tensor zero_mean_65_cast_fp16 = sub(x = inputs_65_cast_fp16, y = channels_mean_65_cast_fp16)[name = tensor("zero_mean_65_cast_fp16")]; + tensor zero_mean_sq_65_cast_fp16 = mul(x = zero_mean_65_cast_fp16, y = zero_mean_65_cast_fp16)[name = tensor("zero_mean_sq_65_cast_fp16")]; + tensor var_3355 = const()[name = tensor("op_3355"), val = tensor([1])]; + tensor var_3356_cast_fp16 = reduce_mean(axes = var_3355, keep_dims = var_3121, x = zero_mean_sq_65_cast_fp16)[name = tensor("op_3356_cast_fp16")]; + tensor var_3357_to_fp16 = const()[name = tensor("op_3357_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3358_cast_fp16 = add(x = var_3356_cast_fp16, y = var_3357_to_fp16)[name = tensor("op_3358_cast_fp16")]; + tensor denom_65_epsilon_0_to_fp16 = const()[name = tensor("denom_65_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_65_cast_fp16 = rsqrt(epsilon = denom_65_epsilon_0_to_fp16, x = var_3358_cast_fp16)[name = tensor("denom_65_cast_fp16")]; + tensor out_65_cast_fp16 = mul(x = zero_mean_65_cast_fp16, y = denom_65_cast_fp16)[name = tensor("out_65_cast_fp16")]; + tensor var_3362_to_fp16 = const()[name = tensor("op_3362_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(696981312)))]; + tensor var_3363_cast_fp16 = add(x = out_65_cast_fp16, y = var_3362_to_fp16)[name = tensor("op_3363_cast_fp16")]; + tensor var_3365_to_fp16 = const()[name = tensor("op_3365_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(696982656)))]; + tensor input_387_cast_fp16 = mul(x = var_3363_cast_fp16, y = var_3365_to_fp16)[name = tensor("input_387_cast_fp16")]; + tensor var_3373 = const()[name = tensor("op_3373"), val = tensor([1, 1])]; + tensor var_3375 = const()[name = tensor("op_3375"), val = tensor([1, 1])]; + tensor var_3377_pad_type_0 = const()[name = tensor("op_3377_pad_type_0"), val = tensor("custom")]; + tensor var_3377_pad_0 = const()[name = tensor("op_3377_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(696984000)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(703537664)))]; + tensor var_3377_cast_fp16 = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_3375, groups = var_3126, pad = var_3377_pad_0, pad_type = var_3377_pad_type_0, strides = var_3373, weight = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_387_cast_fp16)[name = tensor("op_3377_cast_fp16")]; + tensor var_3378_split_sizes_0 = const()[name = tensor("op_3378_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_3378_axis_0 = const()[name = tensor("op_3378_axis_0"), val = tensor(1)]; + tensor var_3378_cast_fp16_0, tensor var_3378_cast_fp16_1 = split(axis = var_3378_axis_0, split_sizes = var_3378_split_sizes_0, x = var_3377_cast_fp16)[name = tensor("op_3378_cast_fp16")]; + tensor var_3380_mode_0 = const()[name = tensor("op_3380_mode_0"), val = tensor("EXACT")]; + tensor var_3380_cast_fp16 = gelu(mode = var_3380_mode_0, x = var_3378_cast_fp16_1)[name = tensor("op_3380_cast_fp16")]; + tensor input_389_cast_fp16 = mul(x = var_3378_cast_fp16_0, y = var_3380_cast_fp16)[name = tensor("input_389_cast_fp16")]; + tensor var_3384 = const()[name = tensor("op_3384"), val = tensor([1, 1])]; + tensor var_3386 = const()[name = tensor("op_3386"), val = tensor([1, 1])]; + tensor var_3388_pad_type_0 = const()[name = tensor("op_3388_pad_type_0"), val = tensor("custom")]; + tensor var_3388_pad_0 = const()[name = tensor("op_3388_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(703547968)))]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(706824832)))]; + tensor var_3388_cast_fp16 = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3386, groups = var_3126, pad = var_3388_pad_0, pad_type = var_3388_pad_type_0, strides = var_3384, weight = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_389_cast_fp16)[name = tensor("op_3388_cast_fp16")]; + tensor hidden_states_233_cast_fp16 = add(x = var_3388_cast_fp16, y = inputs_65_cast_fp16)[name = tensor("hidden_states_233_cast_fp16")]; + tensor var_3390 = const()[name = tensor("op_3390"), val = tensor([2, 640, 24, 40])]; + tensor input_391_cast_fp16 = reshape(shape = var_3390, x = hidden_states_233_cast_fp16)[name = tensor("input_391_cast_fp16")]; + tensor var_3394 = const()[name = tensor("op_3394"), val = tensor([1, 1])]; + tensor var_3396 = const()[name = tensor("op_3396"), val = tensor([1, 1])]; + tensor hidden_states_235_pad_type_0 = const()[name = tensor("hidden_states_235_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_235_pad_0 = const()[name = tensor("hidden_states_235_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(706826176)))]; + tensor up_blocks_2_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(707645440)))]; + tensor hidden_states_235_cast_fp16 = conv(bias = up_blocks_2_attentions_0_proj_out_bias_to_fp16, dilations = var_3396, groups = var_3126, pad = hidden_states_235_pad_0, pad_type = hidden_states_235_pad_type_0, strides = var_3394, weight = up_blocks_2_attentions_0_proj_out_weight_to_fp16, x = input_391_cast_fp16)[name = tensor("hidden_states_235_cast_fp16")]; + tensor hidden_states_237_cast_fp16 = add(x = hidden_states_235_cast_fp16, y = hidden_states_223_cast_fp16)[name = tensor("hidden_states_237_cast_fp16")]; + tensor input_393_interleave_0 = const()[name = tensor("input_393_interleave_0"), val = tensor(false)]; + tensor cast_8 = cast(dtype = cast_7_dtype_0, x = input_89_cast_fp16)[name = tensor("cast_8")]; + tensor input_393_cast_fp16 = concat(axis = var_3126, interleave = input_393_interleave_0, values = (hidden_states_237_cast_fp16, cast_8))[name = tensor("input_393_cast_fp16")]; + tensor reshape_180_shape_0 = const()[name = tensor("reshape_180_shape_0"), val = tensor([2, 32, 40, 24, 40])]; + tensor reshape_180_cast_fp16 = reshape(shape = reshape_180_shape_0, x = input_393_cast_fp16)[name = tensor("reshape_180_cast_fp16")]; + tensor reduce_mean_135_axes_0 = const()[name = tensor("reduce_mean_135_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_135_keep_dims_0 = const()[name = tensor("reduce_mean_135_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_135_cast_fp16 = reduce_mean(axes = reduce_mean_135_axes_0, keep_dims = reduce_mean_135_keep_dims_0, x = reshape_180_cast_fp16)[name = tensor("reduce_mean_135_cast_fp16")]; + tensor sub_90_cast_fp16 = sub(x = reshape_180_cast_fp16, y = reduce_mean_135_cast_fp16)[name = tensor("sub_90_cast_fp16")]; + tensor square_45_cast_fp16 = square(x = sub_90_cast_fp16)[name = tensor("square_45_cast_fp16")]; + tensor reduce_mean_137_axes_0 = const()[name = tensor("reduce_mean_137_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_137_keep_dims_0 = const()[name = tensor("reduce_mean_137_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_137_cast_fp16 = reduce_mean(axes = reduce_mean_137_axes_0, keep_dims = reduce_mean_137_keep_dims_0, x = square_45_cast_fp16)[name = tensor("reduce_mean_137_cast_fp16")]; + tensor add_90_y_0_to_fp16 = const()[name = tensor("add_90_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_90_cast_fp16 = add(x = reduce_mean_137_cast_fp16, y = add_90_y_0_to_fp16)[name = tensor("add_90_cast_fp16")]; + tensor sqrt_45_cast_fp16 = sqrt(x = add_90_cast_fp16)[name = tensor("sqrt_45_cast_fp16")]; + tensor real_div_45_cast_fp16 = real_div(x = sub_90_cast_fp16, y = sqrt_45_cast_fp16)[name = tensor("real_div_45_cast_fp16")]; + tensor reshape_181_shape_0 = const()[name = tensor("reshape_181_shape_0"), val = tensor([2, 1280, 24, 40])]; + tensor reshape_181_cast_fp16 = reshape(shape = reshape_181_shape_0, x = real_div_45_cast_fp16)[name = tensor("reshape_181_cast_fp16")]; + tensor add_91_gamma_0_to_fp16 = const()[name = tensor("add_91_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(707646784)))]; + tensor add_91_beta_0_to_fp16 = const()[name = tensor("add_91_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(707649408)))]; + tensor add_91_epsilon_0_to_fp16 = const()[name = tensor("add_91_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_91_cast_fp16 = batch_norm(beta = add_91_beta_0_to_fp16, epsilon = add_91_epsilon_0_to_fp16, gamma = add_91_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_181_cast_fp16)[name = tensor("add_91_cast_fp16")]; + tensor input_397_cast_fp16 = silu(x = add_91_cast_fp16)[name = tensor("input_397_cast_fp16")]; + tensor var_3414 = const()[name = tensor("op_3414"), val = tensor([1, 1])]; + tensor var_3416 = const()[name = tensor("op_3416"), val = tensor([1, 1])]; + tensor hidden_states_239_pad_type_0 = const()[name = tensor("hidden_states_239_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_239_pad_0 = const()[name = tensor("hidden_states_239_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(707652032)))]; + tensor up_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(722397696)))]; + tensor hidden_states_239_cast_fp16 = conv(bias = up_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_3416, groups = var_3126, pad = hidden_states_239_pad_0, pad_type = hidden_states_239_pad_type_0, strides = var_3414, weight = up_blocks_2_resnets_1_conv1_weight_to_fp16, x = input_397_cast_fp16)[name = tensor("hidden_states_239_cast_fp16")]; + tensor var_3422 = const()[name = tensor("op_3422"), val = tensor([1, 1])]; + tensor var_3424 = const()[name = tensor("op_3424"), val = tensor([1, 1])]; + tensor temb_35_pad_type_0 = const()[name = tensor("temb_35_pad_type_0"), val = tensor("custom")]; + tensor temb_35_pad_0 = const()[name = tensor("temb_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(722399040)))]; + tensor up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(724037504)))]; + tensor temb_35_cast_fp16 = conv(bias = up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_3424, groups = var_3126, pad = temb_35_pad_0, pad_type = temb_35_pad_type_0, strides = var_3422, weight = up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16, x = cast_12)[name = tensor("temb_35_cast_fp16")]; + tensor input_401_cast_fp16 = add(x = hidden_states_239_cast_fp16, y = temb_35_cast_fp16)[name = tensor("input_401_cast_fp16")]; + tensor reshape_184_shape_0 = const()[name = tensor("reshape_184_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_184_cast_fp16 = reshape(shape = reshape_184_shape_0, x = input_401_cast_fp16)[name = tensor("reshape_184_cast_fp16")]; + tensor reduce_mean_138_axes_0 = const()[name = tensor("reduce_mean_138_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_138_keep_dims_0 = const()[name = tensor("reduce_mean_138_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_138_cast_fp16 = reduce_mean(axes = reduce_mean_138_axes_0, keep_dims = reduce_mean_138_keep_dims_0, x = reshape_184_cast_fp16)[name = tensor("reduce_mean_138_cast_fp16")]; + tensor sub_92_cast_fp16 = sub(x = reshape_184_cast_fp16, y = reduce_mean_138_cast_fp16)[name = tensor("sub_92_cast_fp16")]; + tensor square_46_cast_fp16 = square(x = sub_92_cast_fp16)[name = tensor("square_46_cast_fp16")]; + tensor reduce_mean_140_axes_0 = const()[name = tensor("reduce_mean_140_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_140_keep_dims_0 = const()[name = tensor("reduce_mean_140_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_140_cast_fp16 = reduce_mean(axes = reduce_mean_140_axes_0, keep_dims = reduce_mean_140_keep_dims_0, x = square_46_cast_fp16)[name = tensor("reduce_mean_140_cast_fp16")]; + tensor add_92_y_0_to_fp16 = const()[name = tensor("add_92_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_92_cast_fp16 = add(x = reduce_mean_140_cast_fp16, y = add_92_y_0_to_fp16)[name = tensor("add_92_cast_fp16")]; + tensor sqrt_46_cast_fp16 = sqrt(x = add_92_cast_fp16)[name = tensor("sqrt_46_cast_fp16")]; + tensor real_div_46_cast_fp16 = real_div(x = sub_92_cast_fp16, y = sqrt_46_cast_fp16)[name = tensor("real_div_46_cast_fp16")]; + tensor reshape_185_shape_0 = const()[name = tensor("reshape_185_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_185_cast_fp16 = reshape(shape = reshape_185_shape_0, x = real_div_46_cast_fp16)[name = tensor("reshape_185_cast_fp16")]; + tensor add_93_gamma_0_to_fp16 = const()[name = tensor("add_93_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(724038848)))]; + tensor add_93_beta_0_to_fp16 = const()[name = tensor("add_93_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(724040192)))]; + tensor add_93_epsilon_0_to_fp16 = const()[name = tensor("add_93_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_93_cast_fp16 = batch_norm(beta = add_93_beta_0_to_fp16, epsilon = add_93_epsilon_0_to_fp16, gamma = add_93_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_185_cast_fp16)[name = tensor("add_93_cast_fp16")]; + tensor input_405_cast_fp16 = silu(x = add_93_cast_fp16)[name = tensor("input_405_cast_fp16")]; + tensor var_3434 = const()[name = tensor("op_3434"), val = tensor([1, 1])]; + tensor var_3436 = const()[name = tensor("op_3436"), val = tensor([1, 1])]; + tensor hidden_states_241_pad_type_0 = const()[name = tensor("hidden_states_241_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_241_pad_0 = const()[name = tensor("hidden_states_241_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(724041536)))]; + tensor up_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(731414400)))]; + tensor hidden_states_241_cast_fp16 = conv(bias = up_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_3436, groups = var_3126, pad = hidden_states_241_pad_0, pad_type = hidden_states_241_pad_type_0, strides = var_3434, weight = up_blocks_2_resnets_1_conv2_weight_to_fp16, x = input_405_cast_fp16)[name = tensor("hidden_states_241_cast_fp16")]; + tensor var_3441 = const()[name = tensor("op_3441"), val = tensor([1, 1])]; + tensor var_3443 = const()[name = tensor("op_3443"), val = tensor([1, 1])]; + tensor x_19_pad_type_0 = const()[name = tensor("x_19_pad_type_0"), val = tensor("custom")]; + tensor x_19_pad_0 = const()[name = tensor("x_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(731415744)))]; + tensor up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(733054208)))]; + tensor x_19_cast_fp16 = conv(bias = up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_3443, groups = var_3126, pad = x_19_pad_0, pad_type = x_19_pad_type_0, strides = var_3441, weight = up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16, x = input_393_cast_fp16)[name = tensor("x_19_cast_fp16")]; + tensor hidden_states_243_cast_fp16 = add(x = x_19_cast_fp16, y = hidden_states_241_cast_fp16)[name = tensor("hidden_states_243_cast_fp16")]; + tensor reshape_188_shape_0 = const()[name = tensor("reshape_188_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_188_cast_fp16 = reshape(shape = reshape_188_shape_0, x = hidden_states_243_cast_fp16)[name = tensor("reshape_188_cast_fp16")]; + tensor reduce_mean_141_axes_0 = const()[name = tensor("reduce_mean_141_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_141_keep_dims_0 = const()[name = tensor("reduce_mean_141_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_141_cast_fp16 = reduce_mean(axes = reduce_mean_141_axes_0, keep_dims = reduce_mean_141_keep_dims_0, x = reshape_188_cast_fp16)[name = tensor("reduce_mean_141_cast_fp16")]; + tensor sub_94_cast_fp16 = sub(x = reshape_188_cast_fp16, y = reduce_mean_141_cast_fp16)[name = tensor("sub_94_cast_fp16")]; + tensor square_47_cast_fp16 = square(x = sub_94_cast_fp16)[name = tensor("square_47_cast_fp16")]; + tensor reduce_mean_143_axes_0 = const()[name = tensor("reduce_mean_143_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_143_keep_dims_0 = const()[name = tensor("reduce_mean_143_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_143_cast_fp16 = reduce_mean(axes = reduce_mean_143_axes_0, keep_dims = reduce_mean_143_keep_dims_0, x = square_47_cast_fp16)[name = tensor("reduce_mean_143_cast_fp16")]; + tensor add_94_y_0_to_fp16 = const()[name = tensor("add_94_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_94_cast_fp16 = add(x = reduce_mean_143_cast_fp16, y = add_94_y_0_to_fp16)[name = tensor("add_94_cast_fp16")]; + tensor sqrt_47_cast_fp16 = sqrt(x = add_94_cast_fp16)[name = tensor("sqrt_47_cast_fp16")]; + tensor real_div_47_cast_fp16 = real_div(x = sub_94_cast_fp16, y = sqrt_47_cast_fp16)[name = tensor("real_div_47_cast_fp16")]; + tensor reshape_189_shape_0 = const()[name = tensor("reshape_189_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_189_cast_fp16 = reshape(shape = reshape_189_shape_0, x = real_div_47_cast_fp16)[name = tensor("reshape_189_cast_fp16")]; + tensor add_95_gamma_0_to_fp16 = const()[name = tensor("add_95_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(733055552)))]; + tensor add_95_beta_0_to_fp16 = const()[name = tensor("add_95_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(733056896)))]; + tensor add_95_epsilon_0_to_fp16 = const()[name = tensor("add_95_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_95_cast_fp16 = batch_norm(beta = add_95_beta_0_to_fp16, epsilon = add_95_epsilon_0_to_fp16, gamma = add_95_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_189_cast_fp16)[name = tensor("add_95_cast_fp16")]; + tensor var_3463 = const()[name = tensor("op_3463"), val = tensor([1, 1])]; + tensor var_3465 = const()[name = tensor("op_3465"), val = tensor([1, 1])]; + tensor hidden_states_245_pad_type_0 = const()[name = tensor("hidden_states_245_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_245_pad_0 = const()[name = tensor("hidden_states_245_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(733058240)))]; + tensor up_blocks_2_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(733877504)))]; + tensor hidden_states_245_cast_fp16 = conv(bias = up_blocks_2_attentions_1_proj_in_bias_to_fp16, dilations = var_3465, groups = var_3126, pad = hidden_states_245_pad_0, pad_type = hidden_states_245_pad_type_0, strides = var_3463, weight = up_blocks_2_attentions_1_proj_in_weight_to_fp16, x = add_95_cast_fp16)[name = tensor("hidden_states_245_cast_fp16")]; + tensor var_3470 = const()[name = tensor("op_3470"), val = tensor([2, 640, 1, 960])]; + tensor inputs_67_cast_fp16 = reshape(shape = var_3470, x = hidden_states_245_cast_fp16)[name = tensor("inputs_67_cast_fp16")]; + tensor var_3480 = const()[name = tensor("op_3480"), val = tensor([1])]; + tensor channels_mean_67_cast_fp16 = reduce_mean(axes = var_3480, keep_dims = var_3121, x = inputs_67_cast_fp16)[name = tensor("channels_mean_67_cast_fp16")]; + tensor zero_mean_67_cast_fp16 = sub(x = inputs_67_cast_fp16, y = channels_mean_67_cast_fp16)[name = tensor("zero_mean_67_cast_fp16")]; + tensor zero_mean_sq_67_cast_fp16 = mul(x = zero_mean_67_cast_fp16, y = zero_mean_67_cast_fp16)[name = tensor("zero_mean_sq_67_cast_fp16")]; + tensor var_3484 = const()[name = tensor("op_3484"), val = tensor([1])]; + tensor var_3485_cast_fp16 = reduce_mean(axes = var_3484, keep_dims = var_3121, x = zero_mean_sq_67_cast_fp16)[name = tensor("op_3485_cast_fp16")]; + tensor var_3486_to_fp16 = const()[name = tensor("op_3486_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3487_cast_fp16 = add(x = var_3485_cast_fp16, y = var_3486_to_fp16)[name = tensor("op_3487_cast_fp16")]; + tensor denom_67_epsilon_0_to_fp16 = const()[name = tensor("denom_67_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_67_cast_fp16 = rsqrt(epsilon = denom_67_epsilon_0_to_fp16, x = var_3487_cast_fp16)[name = tensor("denom_67_cast_fp16")]; + tensor out_67_cast_fp16 = mul(x = zero_mean_67_cast_fp16, y = denom_67_cast_fp16)[name = tensor("out_67_cast_fp16")]; + tensor var_3491_to_fp16 = const()[name = tensor("op_3491_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(733878848)))]; + tensor var_3492_cast_fp16 = add(x = out_67_cast_fp16, y = var_3491_to_fp16)[name = tensor("op_3492_cast_fp16")]; + tensor var_3494_to_fp16 = const()[name = tensor("op_3494_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(733880192)))]; + tensor hidden_states_247_cast_fp16 = mul(x = var_3492_cast_fp16, y = var_3494_to_fp16)[name = tensor("hidden_states_247_cast_fp16")]; + tensor var_3501 = const()[name = tensor("op_3501"), val = tensor([1, 1])]; + tensor var_3503 = const()[name = tensor("op_3503"), val = tensor([1, 1])]; + tensor q_45_pad_type_0 = const()[name = tensor("q_45_pad_type_0"), val = tensor("custom")]; + tensor q_45_pad_0 = const()[name = tensor("q_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(733881536)))]; + tensor q_45_cast_fp16 = conv(dilations = var_3503, groups = var_3126, pad = q_45_pad_0, pad_type = q_45_pad_type_0, strides = var_3501, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_247_cast_fp16)[name = tensor("q_45_cast_fp16")]; + tensor var_3507 = const()[name = tensor("op_3507"), val = tensor([1, 1])]; + tensor var_3509 = const()[name = tensor("op_3509"), val = tensor([1, 1])]; + tensor k_45_pad_type_0 = const()[name = tensor("k_45_pad_type_0"), val = tensor("custom")]; + tensor k_45_pad_0 = const()[name = tensor("k_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(734700800)))]; + tensor k_45_cast_fp16 = conv(dilations = var_3509, groups = var_3126, pad = k_45_pad_0, pad_type = k_45_pad_type_0, strides = var_3507, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_247_cast_fp16)[name = tensor("k_45_cast_fp16")]; + tensor var_3513 = const()[name = tensor("op_3513"), val = tensor([1, 1])]; + tensor var_3515 = const()[name = tensor("op_3515"), val = tensor([1, 1])]; + tensor v_45_pad_type_0 = const()[name = tensor("v_45_pad_type_0"), val = tensor("custom")]; + tensor v_45_pad_0 = const()[name = tensor("v_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(735520064)))]; + tensor v_45_cast_fp16 = conv(dilations = var_3515, groups = var_3126, pad = v_45_pad_0, pad_type = v_45_pad_type_0, strides = var_3513, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_247_cast_fp16)[name = tensor("v_45_cast_fp16")]; + tensor var_3519 = const()[name = tensor("op_3519"), val = tensor([2, 10, 64, -1])]; + tensor var_3520_cast_fp16 = reshape(shape = var_3519, x = q_45_cast_fp16)[name = tensor("op_3520_cast_fp16")]; + tensor var_3521 = const()[name = tensor("op_3521"), val = tensor([2, 10, 64, -1])]; + tensor var_3522_cast_fp16 = reshape(shape = var_3521, x = k_45_cast_fp16)[name = tensor("op_3522_cast_fp16")]; + tensor var_3523 = const()[name = tensor("op_3523"), val = tensor([2, 10, 64, -1])]; + tensor var_3524_cast_fp16 = reshape(shape = var_3523, x = v_45_cast_fp16)[name = tensor("op_3524_cast_fp16")]; + tensor attn_weights_89_transpose_x_0 = const()[name = tensor("attn_weights_89_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_89_transpose_y_0 = const()[name = tensor("attn_weights_89_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_89_cast_fp16 = matmul(transpose_x = attn_weights_89_transpose_x_0, transpose_y = attn_weights_89_transpose_y_0, x = var_3520_cast_fp16, y = var_3522_cast_fp16)[name = tensor("attn_weights_89_cast_fp16")]; + tensor attn_weights_91_cast_fp16 = mul(x = attn_weights_89_cast_fp16, y = var_3117_to_fp16)[name = tensor("attn_weights_91_cast_fp16")]; + tensor var_3528_cast_fp16 = softmax(axis = var_3110, x = attn_weights_91_cast_fp16)[name = tensor("op_3528_cast_fp16")]; + tensor attn_45_transpose_x_0 = const()[name = tensor("attn_45_transpose_x_0"), val = tensor(false)]; + tensor attn_45_transpose_y_0 = const()[name = tensor("attn_45_transpose_y_0"), val = tensor(true)]; + tensor attn_45_cast_fp16 = matmul(transpose_x = attn_45_transpose_x_0, transpose_y = attn_45_transpose_y_0, x = var_3524_cast_fp16, y = var_3528_cast_fp16)[name = tensor("attn_45_cast_fp16")]; + tensor var_3532 = const()[name = tensor("op_3532"), val = tensor([2, 640, 1, -1])]; + tensor input_409_cast_fp16 = reshape(shape = var_3532, x = attn_45_cast_fp16)[name = tensor("input_409_cast_fp16")]; + tensor var_3537 = const()[name = tensor("op_3537"), val = tensor([1, 1])]; + tensor var_3539 = const()[name = tensor("op_3539"), val = tensor([1, 1])]; + tensor var_3541_pad_type_0 = const()[name = tensor("op_3541_pad_type_0"), val = tensor("custom")]; + tensor var_3541_pad_0 = const()[name = tensor("op_3541_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(736339328)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(737158592)))]; + tensor var_3541_cast_fp16 = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3539, groups = var_3126, pad = var_3541_pad_0, pad_type = var_3541_pad_type_0, strides = var_3537, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_409_cast_fp16)[name = tensor("op_3541_cast_fp16")]; + tensor inputs_69_cast_fp16 = add(x = var_3541_cast_fp16, y = inputs_67_cast_fp16)[name = tensor("inputs_69_cast_fp16")]; + tensor var_3545 = const()[name = tensor("op_3545"), val = tensor([1])]; + tensor channels_mean_69_cast_fp16 = reduce_mean(axes = var_3545, keep_dims = var_3121, x = inputs_69_cast_fp16)[name = tensor("channels_mean_69_cast_fp16")]; + tensor zero_mean_69_cast_fp16 = sub(x = inputs_69_cast_fp16, y = channels_mean_69_cast_fp16)[name = tensor("zero_mean_69_cast_fp16")]; + tensor zero_mean_sq_69_cast_fp16 = mul(x = zero_mean_69_cast_fp16, y = zero_mean_69_cast_fp16)[name = tensor("zero_mean_sq_69_cast_fp16")]; + tensor var_3549 = const()[name = tensor("op_3549"), val = tensor([1])]; + tensor var_3550_cast_fp16 = reduce_mean(axes = var_3549, keep_dims = var_3121, x = zero_mean_sq_69_cast_fp16)[name = tensor("op_3550_cast_fp16")]; + tensor var_3551_to_fp16 = const()[name = tensor("op_3551_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3552_cast_fp16 = add(x = var_3550_cast_fp16, y = var_3551_to_fp16)[name = tensor("op_3552_cast_fp16")]; + tensor denom_69_epsilon_0_to_fp16 = const()[name = tensor("denom_69_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_69_cast_fp16 = rsqrt(epsilon = denom_69_epsilon_0_to_fp16, x = var_3552_cast_fp16)[name = tensor("denom_69_cast_fp16")]; + tensor out_69_cast_fp16 = mul(x = zero_mean_69_cast_fp16, y = denom_69_cast_fp16)[name = tensor("out_69_cast_fp16")]; + tensor var_3556_to_fp16 = const()[name = tensor("op_3556_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(737159936)))]; + tensor var_3557_cast_fp16 = add(x = out_69_cast_fp16, y = var_3556_to_fp16)[name = tensor("op_3557_cast_fp16")]; + tensor var_3559_to_fp16 = const()[name = tensor("op_3559_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(737161280)))]; + tensor hidden_states_249_cast_fp16 = mul(x = var_3557_cast_fp16, y = var_3559_to_fp16)[name = tensor("hidden_states_249_cast_fp16")]; + tensor var_3566 = const()[name = tensor("op_3566"), val = tensor([1, 1])]; + tensor var_3568 = const()[name = tensor("op_3568"), val = tensor([1, 1])]; + tensor q_47_pad_type_0 = const()[name = tensor("q_47_pad_type_0"), val = tensor("custom")]; + tensor q_47_pad_0 = const()[name = tensor("q_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(737162624)))]; + tensor q_47_cast_fp16 = conv(dilations = var_3568, groups = var_3126, pad = q_47_pad_0, pad_type = q_47_pad_type_0, strides = var_3566, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_249_cast_fp16)[name = tensor("q_47_cast_fp16")]; + tensor var_3572 = const()[name = tensor("op_3572"), val = tensor([1, 1])]; + tensor var_3574 = const()[name = tensor("op_3574"), val = tensor([1, 1])]; + tensor k_47_pad_type_0 = const()[name = tensor("k_47_pad_type_0"), val = tensor("custom")]; + tensor k_47_pad_0 = const()[name = tensor("k_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(737981888)))]; + tensor k_47_cast_fp16 = conv(dilations = var_3574, groups = var_3126, pad = k_47_pad_0, pad_type = k_47_pad_type_0, strides = var_3572, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_47_cast_fp16")]; + tensor var_3578 = const()[name = tensor("op_3578"), val = tensor([1, 1])]; + tensor var_3580 = const()[name = tensor("op_3580"), val = tensor([1, 1])]; + tensor v_47_pad_type_0 = const()[name = tensor("v_47_pad_type_0"), val = tensor("custom")]; + tensor v_47_pad_0 = const()[name = tensor("v_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(739292672)))]; + tensor v_47_cast_fp16 = conv(dilations = var_3580, groups = var_3126, pad = v_47_pad_0, pad_type = v_47_pad_type_0, strides = var_3578, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_47_cast_fp16")]; + tensor var_3584 = const()[name = tensor("op_3584"), val = tensor([2, 10, 64, -1])]; + tensor var_3585_cast_fp16 = reshape(shape = var_3584, x = q_47_cast_fp16)[name = tensor("op_3585_cast_fp16")]; + tensor var_3586 = const()[name = tensor("op_3586"), val = tensor([2, 10, 64, -1])]; + tensor var_3587_cast_fp16 = reshape(shape = var_3586, x = k_47_cast_fp16)[name = tensor("op_3587_cast_fp16")]; + tensor var_3588 = const()[name = tensor("op_3588"), val = tensor([2, 10, 64, -1])]; + tensor var_3589_cast_fp16 = reshape(shape = var_3588, x = v_47_cast_fp16)[name = tensor("op_3589_cast_fp16")]; + tensor attn_weights_93_transpose_x_0 = const()[name = tensor("attn_weights_93_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_93_transpose_y_0 = const()[name = tensor("attn_weights_93_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_93_cast_fp16 = matmul(transpose_x = attn_weights_93_transpose_x_0, transpose_y = attn_weights_93_transpose_y_0, x = var_3585_cast_fp16, y = var_3587_cast_fp16)[name = tensor("attn_weights_93_cast_fp16")]; + tensor attn_weights_95_cast_fp16 = mul(x = attn_weights_93_cast_fp16, y = var_3117_to_fp16)[name = tensor("attn_weights_95_cast_fp16")]; + tensor var_3593_cast_fp16 = softmax(axis = var_3110, x = attn_weights_95_cast_fp16)[name = tensor("op_3593_cast_fp16")]; + tensor attn_47_transpose_x_0 = const()[name = tensor("attn_47_transpose_x_0"), val = tensor(false)]; + tensor attn_47_transpose_y_0 = const()[name = tensor("attn_47_transpose_y_0"), val = tensor(true)]; + tensor attn_47_cast_fp16 = matmul(transpose_x = attn_47_transpose_x_0, transpose_y = attn_47_transpose_y_0, x = var_3589_cast_fp16, y = var_3593_cast_fp16)[name = tensor("attn_47_cast_fp16")]; + tensor var_3597 = const()[name = tensor("op_3597"), val = tensor([2, 640, 1, -1])]; + tensor input_411_cast_fp16 = reshape(shape = var_3597, x = attn_47_cast_fp16)[name = tensor("input_411_cast_fp16")]; + tensor var_3602 = const()[name = tensor("op_3602"), val = tensor([1, 1])]; + tensor var_3604 = const()[name = tensor("op_3604"), val = tensor([1, 1])]; + tensor var_3606_pad_type_0 = const()[name = tensor("op_3606_pad_type_0"), val = tensor("custom")]; + tensor var_3606_pad_0 = const()[name = tensor("op_3606_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(740603456)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(741422720)))]; + tensor var_3606_cast_fp16 = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3604, groups = var_3126, pad = var_3606_pad_0, pad_type = var_3606_pad_type_0, strides = var_3602, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_411_cast_fp16)[name = tensor("op_3606_cast_fp16")]; + tensor inputs_71_cast_fp16 = add(x = var_3606_cast_fp16, y = inputs_69_cast_fp16)[name = tensor("inputs_71_cast_fp16")]; + tensor var_3610 = const()[name = tensor("op_3610"), val = tensor([1])]; + tensor channels_mean_71_cast_fp16 = reduce_mean(axes = var_3610, keep_dims = var_3121, x = inputs_71_cast_fp16)[name = tensor("channels_mean_71_cast_fp16")]; + tensor zero_mean_71_cast_fp16 = sub(x = inputs_71_cast_fp16, y = channels_mean_71_cast_fp16)[name = tensor("zero_mean_71_cast_fp16")]; + tensor zero_mean_sq_71_cast_fp16 = mul(x = zero_mean_71_cast_fp16, y = zero_mean_71_cast_fp16)[name = tensor("zero_mean_sq_71_cast_fp16")]; + tensor var_3614 = const()[name = tensor("op_3614"), val = tensor([1])]; + tensor var_3615_cast_fp16 = reduce_mean(axes = var_3614, keep_dims = var_3121, x = zero_mean_sq_71_cast_fp16)[name = tensor("op_3615_cast_fp16")]; + tensor var_3616_to_fp16 = const()[name = tensor("op_3616_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3617_cast_fp16 = add(x = var_3615_cast_fp16, y = var_3616_to_fp16)[name = tensor("op_3617_cast_fp16")]; + tensor denom_71_epsilon_0_to_fp16 = const()[name = tensor("denom_71_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_71_cast_fp16 = rsqrt(epsilon = denom_71_epsilon_0_to_fp16, x = var_3617_cast_fp16)[name = tensor("denom_71_cast_fp16")]; + tensor out_71_cast_fp16 = mul(x = zero_mean_71_cast_fp16, y = denom_71_cast_fp16)[name = tensor("out_71_cast_fp16")]; + tensor var_3621_to_fp16 = const()[name = tensor("op_3621_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(741424064)))]; + tensor var_3622_cast_fp16 = add(x = out_71_cast_fp16, y = var_3621_to_fp16)[name = tensor("op_3622_cast_fp16")]; + tensor var_3624_to_fp16 = const()[name = tensor("op_3624_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(741425408)))]; + tensor input_413_cast_fp16 = mul(x = var_3622_cast_fp16, y = var_3624_to_fp16)[name = tensor("input_413_cast_fp16")]; + tensor var_3632 = const()[name = tensor("op_3632"), val = tensor([1, 1])]; + tensor var_3634 = const()[name = tensor("op_3634"), val = tensor([1, 1])]; + tensor var_3636_pad_type_0 = const()[name = tensor("op_3636_pad_type_0"), val = tensor("custom")]; + tensor var_3636_pad_0 = const()[name = tensor("op_3636_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(741426752)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(747980416)))]; + tensor var_3636_cast_fp16 = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_3634, groups = var_3126, pad = var_3636_pad_0, pad_type = var_3636_pad_type_0, strides = var_3632, weight = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_413_cast_fp16)[name = tensor("op_3636_cast_fp16")]; + tensor var_3637_split_sizes_0 = const()[name = tensor("op_3637_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_3637_axis_0 = const()[name = tensor("op_3637_axis_0"), val = tensor(1)]; + tensor var_3637_cast_fp16_0, tensor var_3637_cast_fp16_1 = split(axis = var_3637_axis_0, split_sizes = var_3637_split_sizes_0, x = var_3636_cast_fp16)[name = tensor("op_3637_cast_fp16")]; + tensor var_3639_mode_0 = const()[name = tensor("op_3639_mode_0"), val = tensor("EXACT")]; + tensor var_3639_cast_fp16 = gelu(mode = var_3639_mode_0, x = var_3637_cast_fp16_1)[name = tensor("op_3639_cast_fp16")]; + tensor input_415_cast_fp16 = mul(x = var_3637_cast_fp16_0, y = var_3639_cast_fp16)[name = tensor("input_415_cast_fp16")]; + tensor var_3643 = const()[name = tensor("op_3643"), val = tensor([1, 1])]; + tensor var_3645 = const()[name = tensor("op_3645"), val = tensor([1, 1])]; + tensor var_3647_pad_type_0 = const()[name = tensor("op_3647_pad_type_0"), val = tensor("custom")]; + tensor var_3647_pad_0 = const()[name = tensor("op_3647_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(747990720)))]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(751267584)))]; + tensor var_3647_cast_fp16 = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3645, groups = var_3126, pad = var_3647_pad_0, pad_type = var_3647_pad_type_0, strides = var_3643, weight = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_415_cast_fp16)[name = tensor("op_3647_cast_fp16")]; + tensor hidden_states_253_cast_fp16 = add(x = var_3647_cast_fp16, y = inputs_71_cast_fp16)[name = tensor("hidden_states_253_cast_fp16")]; + tensor var_3649 = const()[name = tensor("op_3649"), val = tensor([2, 640, 24, 40])]; + tensor input_417_cast_fp16 = reshape(shape = var_3649, x = hidden_states_253_cast_fp16)[name = tensor("input_417_cast_fp16")]; + tensor var_3653 = const()[name = tensor("op_3653"), val = tensor([1, 1])]; + tensor var_3655 = const()[name = tensor("op_3655"), val = tensor([1, 1])]; + tensor hidden_states_255_pad_type_0 = const()[name = tensor("hidden_states_255_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_255_pad_0 = const()[name = tensor("hidden_states_255_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(751268928)))]; + tensor up_blocks_2_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(752088192)))]; + tensor hidden_states_255_cast_fp16 = conv(bias = up_blocks_2_attentions_1_proj_out_bias_to_fp16, dilations = var_3655, groups = var_3126, pad = hidden_states_255_pad_0, pad_type = hidden_states_255_pad_type_0, strides = var_3653, weight = up_blocks_2_attentions_1_proj_out_weight_to_fp16, x = input_417_cast_fp16)[name = tensor("hidden_states_255_cast_fp16")]; + tensor hidden_states_257_cast_fp16 = add(x = hidden_states_255_cast_fp16, y = hidden_states_243_cast_fp16)[name = tensor("hidden_states_257_cast_fp16")]; + tensor input_419_interleave_0 = const()[name = tensor("input_419_interleave_0"), val = tensor(false)]; + tensor cast_9 = cast(dtype = cast_8_dtype_0, x = input_63_cast_fp16)[name = tensor("cast_9")]; + tensor input_419_cast_fp16 = concat(axis = var_3126, interleave = input_419_interleave_0, values = (hidden_states_257_cast_fp16, cast_9))[name = tensor("input_419_cast_fp16")]; + tensor reshape_192_shape_0 = const()[name = tensor("reshape_192_shape_0"), val = tensor([2, 32, 30, 24, 40])]; + tensor reshape_192_cast_fp16 = reshape(shape = reshape_192_shape_0, x = input_419_cast_fp16)[name = tensor("reshape_192_cast_fp16")]; + tensor reduce_mean_144_axes_0 = const()[name = tensor("reduce_mean_144_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_144_keep_dims_0 = const()[name = tensor("reduce_mean_144_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_144_cast_fp16 = reduce_mean(axes = reduce_mean_144_axes_0, keep_dims = reduce_mean_144_keep_dims_0, x = reshape_192_cast_fp16)[name = tensor("reduce_mean_144_cast_fp16")]; + tensor sub_96_cast_fp16 = sub(x = reshape_192_cast_fp16, y = reduce_mean_144_cast_fp16)[name = tensor("sub_96_cast_fp16")]; + tensor square_48_cast_fp16 = square(x = sub_96_cast_fp16)[name = tensor("square_48_cast_fp16")]; + tensor reduce_mean_146_axes_0 = const()[name = tensor("reduce_mean_146_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_146_keep_dims_0 = const()[name = tensor("reduce_mean_146_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_146_cast_fp16 = reduce_mean(axes = reduce_mean_146_axes_0, keep_dims = reduce_mean_146_keep_dims_0, x = square_48_cast_fp16)[name = tensor("reduce_mean_146_cast_fp16")]; + tensor add_96_y_0_to_fp16 = const()[name = tensor("add_96_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_96_cast_fp16 = add(x = reduce_mean_146_cast_fp16, y = add_96_y_0_to_fp16)[name = tensor("add_96_cast_fp16")]; + tensor sqrt_48_cast_fp16 = sqrt(x = add_96_cast_fp16)[name = tensor("sqrt_48_cast_fp16")]; + tensor real_div_48_cast_fp16 = real_div(x = sub_96_cast_fp16, y = sqrt_48_cast_fp16)[name = tensor("real_div_48_cast_fp16")]; + tensor reshape_193_shape_0 = const()[name = tensor("reshape_193_shape_0"), val = tensor([2, 960, 24, 40])]; + tensor reshape_193_cast_fp16 = reshape(shape = reshape_193_shape_0, x = real_div_48_cast_fp16)[name = tensor("reshape_193_cast_fp16")]; + tensor add_97_mean_0_to_fp16 = const()[name = tensor("add_97_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(752089536)))]; + tensor add_97_variance_0_to_fp16 = const()[name = tensor("add_97_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(752091520)))]; + tensor add_97_gamma_0_to_fp16 = const()[name = tensor("add_97_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(752093504)))]; + tensor add_97_beta_0_to_fp16 = const()[name = tensor("add_97_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(752095488)))]; + tensor add_97_epsilon_0_to_fp16 = const()[name = tensor("add_97_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_97_cast_fp16 = batch_norm(beta = add_97_beta_0_to_fp16, epsilon = add_97_epsilon_0_to_fp16, gamma = add_97_gamma_0_to_fp16, mean = add_97_mean_0_to_fp16, variance = add_97_variance_0_to_fp16, x = reshape_193_cast_fp16)[name = tensor("add_97_cast_fp16")]; + tensor input_423_cast_fp16 = silu(x = add_97_cast_fp16)[name = tensor("input_423_cast_fp16")]; + tensor var_3673 = const()[name = tensor("op_3673"), val = tensor([1, 1])]; + tensor var_3675 = const()[name = tensor("op_3675"), val = tensor([1, 1])]; + tensor hidden_states_259_pad_type_0 = const()[name = tensor("hidden_states_259_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_259_pad_0 = const()[name = tensor("hidden_states_259_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(752097472)))]; + tensor up_blocks_2_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(763156736)))]; + tensor hidden_states_259_cast_fp16 = conv(bias = up_blocks_2_resnets_2_conv1_bias_to_fp16, dilations = var_3675, groups = var_3126, pad = hidden_states_259_pad_0, pad_type = hidden_states_259_pad_type_0, strides = var_3673, weight = up_blocks_2_resnets_2_conv1_weight_to_fp16, x = input_423_cast_fp16)[name = tensor("hidden_states_259_cast_fp16")]; + tensor var_3681 = const()[name = tensor("op_3681"), val = tensor([1, 1])]; + tensor var_3683 = const()[name = tensor("op_3683"), val = tensor([1, 1])]; + tensor temb_37_pad_type_0 = const()[name = tensor("temb_37_pad_type_0"), val = tensor("custom")]; + tensor temb_37_pad_0 = const()[name = tensor("temb_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(763158080)))]; + tensor up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(764796544)))]; + tensor temb_37_cast_fp16 = conv(bias = up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_3683, groups = var_3126, pad = temb_37_pad_0, pad_type = temb_37_pad_type_0, strides = var_3681, weight = up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16, x = cast_12)[name = tensor("temb_37_cast_fp16")]; + tensor input_427_cast_fp16 = add(x = hidden_states_259_cast_fp16, y = temb_37_cast_fp16)[name = tensor("input_427_cast_fp16")]; + tensor reshape_196_shape_0 = const()[name = tensor("reshape_196_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_196_cast_fp16 = reshape(shape = reshape_196_shape_0, x = input_427_cast_fp16)[name = tensor("reshape_196_cast_fp16")]; + tensor reduce_mean_147_axes_0 = const()[name = tensor("reduce_mean_147_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_147_keep_dims_0 = const()[name = tensor("reduce_mean_147_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_147_cast_fp16 = reduce_mean(axes = reduce_mean_147_axes_0, keep_dims = reduce_mean_147_keep_dims_0, x = reshape_196_cast_fp16)[name = tensor("reduce_mean_147_cast_fp16")]; + tensor sub_98_cast_fp16 = sub(x = reshape_196_cast_fp16, y = reduce_mean_147_cast_fp16)[name = tensor("sub_98_cast_fp16")]; + tensor square_49_cast_fp16 = square(x = sub_98_cast_fp16)[name = tensor("square_49_cast_fp16")]; + tensor reduce_mean_149_axes_0 = const()[name = tensor("reduce_mean_149_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_149_keep_dims_0 = const()[name = tensor("reduce_mean_149_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_149_cast_fp16 = reduce_mean(axes = reduce_mean_149_axes_0, keep_dims = reduce_mean_149_keep_dims_0, x = square_49_cast_fp16)[name = tensor("reduce_mean_149_cast_fp16")]; + tensor add_98_y_0_to_fp16 = const()[name = tensor("add_98_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_98_cast_fp16 = add(x = reduce_mean_149_cast_fp16, y = add_98_y_0_to_fp16)[name = tensor("add_98_cast_fp16")]; + tensor sqrt_49_cast_fp16 = sqrt(x = add_98_cast_fp16)[name = tensor("sqrt_49_cast_fp16")]; + tensor real_div_49_cast_fp16 = real_div(x = sub_98_cast_fp16, y = sqrt_49_cast_fp16)[name = tensor("real_div_49_cast_fp16")]; + tensor reshape_197_shape_0 = const()[name = tensor("reshape_197_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_197_cast_fp16 = reshape(shape = reshape_197_shape_0, x = real_div_49_cast_fp16)[name = tensor("reshape_197_cast_fp16")]; + tensor add_99_gamma_0_to_fp16 = const()[name = tensor("add_99_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(764797888)))]; + tensor add_99_beta_0_to_fp16 = const()[name = tensor("add_99_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(764799232)))]; + tensor add_99_epsilon_0_to_fp16 = const()[name = tensor("add_99_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_99_cast_fp16 = batch_norm(beta = add_99_beta_0_to_fp16, epsilon = add_99_epsilon_0_to_fp16, gamma = add_99_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_197_cast_fp16)[name = tensor("add_99_cast_fp16")]; + tensor input_431_cast_fp16 = silu(x = add_99_cast_fp16)[name = tensor("input_431_cast_fp16")]; + tensor var_3693 = const()[name = tensor("op_3693"), val = tensor([1, 1])]; + tensor var_3695 = const()[name = tensor("op_3695"), val = tensor([1, 1])]; + tensor hidden_states_261_pad_type_0 = const()[name = tensor("hidden_states_261_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_261_pad_0 = const()[name = tensor("hidden_states_261_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(764800576)))]; + tensor up_blocks_2_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(772173440)))]; + tensor hidden_states_261_cast_fp16 = conv(bias = up_blocks_2_resnets_2_conv2_bias_to_fp16, dilations = var_3695, groups = var_3126, pad = hidden_states_261_pad_0, pad_type = hidden_states_261_pad_type_0, strides = var_3693, weight = up_blocks_2_resnets_2_conv2_weight_to_fp16, x = input_431_cast_fp16)[name = tensor("hidden_states_261_cast_fp16")]; + tensor var_3700 = const()[name = tensor("op_3700"), val = tensor([1, 1])]; + tensor var_3702 = const()[name = tensor("op_3702"), val = tensor([1, 1])]; + 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([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(772174784)))]; + tensor up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(773403648)))]; + tensor x_21_cast_fp16 = conv(bias = up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_3702, groups = var_3126, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = var_3700, weight = up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16, x = input_419_cast_fp16)[name = tensor("x_21_cast_fp16")]; + tensor hidden_states_263_cast_fp16 = add(x = x_21_cast_fp16, y = hidden_states_261_cast_fp16)[name = tensor("hidden_states_263_cast_fp16")]; + tensor reshape_200_shape_0 = const()[name = tensor("reshape_200_shape_0"), val = tensor([2, 32, 20, 24, 40])]; + tensor reshape_200_cast_fp16 = reshape(shape = reshape_200_shape_0, x = hidden_states_263_cast_fp16)[name = tensor("reshape_200_cast_fp16")]; + tensor reduce_mean_150_axes_0 = const()[name = tensor("reduce_mean_150_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_150_keep_dims_0 = const()[name = tensor("reduce_mean_150_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_150_cast_fp16 = reduce_mean(axes = reduce_mean_150_axes_0, keep_dims = reduce_mean_150_keep_dims_0, x = reshape_200_cast_fp16)[name = tensor("reduce_mean_150_cast_fp16")]; + tensor sub_100_cast_fp16 = sub(x = reshape_200_cast_fp16, y = reduce_mean_150_cast_fp16)[name = tensor("sub_100_cast_fp16")]; + tensor square_50_cast_fp16 = square(x = sub_100_cast_fp16)[name = tensor("square_50_cast_fp16")]; + tensor reduce_mean_152_axes_0 = const()[name = tensor("reduce_mean_152_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_152_keep_dims_0 = const()[name = tensor("reduce_mean_152_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_152_cast_fp16 = reduce_mean(axes = reduce_mean_152_axes_0, keep_dims = reduce_mean_152_keep_dims_0, x = square_50_cast_fp16)[name = tensor("reduce_mean_152_cast_fp16")]; + tensor add_100_y_0_to_fp16 = const()[name = tensor("add_100_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_100_cast_fp16 = add(x = reduce_mean_152_cast_fp16, y = add_100_y_0_to_fp16)[name = tensor("add_100_cast_fp16")]; + tensor sqrt_50_cast_fp16 = sqrt(x = add_100_cast_fp16)[name = tensor("sqrt_50_cast_fp16")]; + tensor real_div_50_cast_fp16 = real_div(x = sub_100_cast_fp16, y = sqrt_50_cast_fp16)[name = tensor("real_div_50_cast_fp16")]; + tensor reshape_201_shape_0 = const()[name = tensor("reshape_201_shape_0"), val = tensor([2, 640, 24, 40])]; + tensor reshape_201_cast_fp16 = reshape(shape = reshape_201_shape_0, x = real_div_50_cast_fp16)[name = tensor("reshape_201_cast_fp16")]; + tensor add_101_gamma_0_to_fp16 = const()[name = tensor("add_101_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(773404992)))]; + tensor add_101_beta_0_to_fp16 = const()[name = tensor("add_101_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(773406336)))]; + tensor add_101_epsilon_0_to_fp16 = const()[name = tensor("add_101_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_101_cast_fp16 = batch_norm(beta = add_101_beta_0_to_fp16, epsilon = add_101_epsilon_0_to_fp16, gamma = add_101_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_201_cast_fp16)[name = tensor("add_101_cast_fp16")]; + tensor var_3722 = const()[name = tensor("op_3722"), val = tensor([1, 1])]; + tensor var_3724 = const()[name = tensor("op_3724"), val = tensor([1, 1])]; + tensor hidden_states_265_pad_type_0 = const()[name = tensor("hidden_states_265_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_265_pad_0 = const()[name = tensor("hidden_states_265_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(773407680)))]; + tensor up_blocks_2_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(774226944)))]; + tensor hidden_states_265_cast_fp16 = conv(bias = up_blocks_2_attentions_2_proj_in_bias_to_fp16, dilations = var_3724, groups = var_3126, pad = hidden_states_265_pad_0, pad_type = hidden_states_265_pad_type_0, strides = var_3722, weight = up_blocks_2_attentions_2_proj_in_weight_to_fp16, x = add_101_cast_fp16)[name = tensor("hidden_states_265_cast_fp16")]; + tensor var_3729 = const()[name = tensor("op_3729"), val = tensor([2, 640, 1, 960])]; + tensor inputs_73_cast_fp16 = reshape(shape = var_3729, x = hidden_states_265_cast_fp16)[name = tensor("inputs_73_cast_fp16")]; + tensor var_3739 = const()[name = tensor("op_3739"), val = tensor([1])]; + tensor channels_mean_73_cast_fp16 = reduce_mean(axes = var_3739, keep_dims = var_3121, x = inputs_73_cast_fp16)[name = tensor("channels_mean_73_cast_fp16")]; + tensor zero_mean_73_cast_fp16 = sub(x = inputs_73_cast_fp16, y = channels_mean_73_cast_fp16)[name = tensor("zero_mean_73_cast_fp16")]; + tensor zero_mean_sq_73_cast_fp16 = mul(x = zero_mean_73_cast_fp16, y = zero_mean_73_cast_fp16)[name = tensor("zero_mean_sq_73_cast_fp16")]; + tensor var_3743 = const()[name = tensor("op_3743"), val = tensor([1])]; + tensor var_3744_cast_fp16 = reduce_mean(axes = var_3743, keep_dims = var_3121, x = zero_mean_sq_73_cast_fp16)[name = tensor("op_3744_cast_fp16")]; + tensor var_3745_to_fp16 = const()[name = tensor("op_3745_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3746_cast_fp16 = add(x = var_3744_cast_fp16, y = var_3745_to_fp16)[name = tensor("op_3746_cast_fp16")]; + tensor denom_73_epsilon_0_to_fp16 = const()[name = tensor("denom_73_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_73_cast_fp16 = rsqrt(epsilon = denom_73_epsilon_0_to_fp16, x = var_3746_cast_fp16)[name = tensor("denom_73_cast_fp16")]; + tensor out_73_cast_fp16 = mul(x = zero_mean_73_cast_fp16, y = denom_73_cast_fp16)[name = tensor("out_73_cast_fp16")]; + tensor var_3750_to_fp16 = const()[name = tensor("op_3750_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(774228288)))]; + tensor var_3751_cast_fp16 = add(x = out_73_cast_fp16, y = var_3750_to_fp16)[name = tensor("op_3751_cast_fp16")]; + tensor var_3753_to_fp16 = const()[name = tensor("op_3753_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(774229632)))]; + tensor hidden_states_267_cast_fp16 = mul(x = var_3751_cast_fp16, y = var_3753_to_fp16)[name = tensor("hidden_states_267_cast_fp16")]; + tensor var_3760 = const()[name = tensor("op_3760"), val = tensor([1, 1])]; + tensor var_3762 = const()[name = tensor("op_3762"), val = tensor([1, 1])]; + tensor q_49_pad_type_0 = const()[name = tensor("q_49_pad_type_0"), val = tensor("custom")]; + tensor q_49_pad_0 = const()[name = tensor("q_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(774230976)))]; + tensor q_49_cast_fp16 = conv(dilations = var_3762, groups = var_3126, pad = q_49_pad_0, pad_type = q_49_pad_type_0, strides = var_3760, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_267_cast_fp16)[name = tensor("q_49_cast_fp16")]; + tensor var_3766 = const()[name = tensor("op_3766"), val = tensor([1, 1])]; + tensor var_3768 = const()[name = tensor("op_3768"), val = tensor([1, 1])]; + tensor k_49_pad_type_0 = const()[name = tensor("k_49_pad_type_0"), val = tensor("custom")]; + tensor k_49_pad_0 = const()[name = tensor("k_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(775050240)))]; + tensor k_49_cast_fp16 = conv(dilations = var_3768, groups = var_3126, pad = k_49_pad_0, pad_type = k_49_pad_type_0, strides = var_3766, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_267_cast_fp16)[name = tensor("k_49_cast_fp16")]; + tensor var_3772 = const()[name = tensor("op_3772"), val = tensor([1, 1])]; + tensor var_3774 = const()[name = tensor("op_3774"), val = tensor([1, 1])]; + tensor v_49_pad_type_0 = const()[name = tensor("v_49_pad_type_0"), val = tensor("custom")]; + tensor v_49_pad_0 = const()[name = tensor("v_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(775869504)))]; + tensor v_49_cast_fp16 = conv(dilations = var_3774, groups = var_3126, pad = v_49_pad_0, pad_type = v_49_pad_type_0, strides = var_3772, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_267_cast_fp16)[name = tensor("v_49_cast_fp16")]; + tensor var_3778 = const()[name = tensor("op_3778"), val = tensor([2, 10, 64, -1])]; + tensor var_3779_cast_fp16 = reshape(shape = var_3778, x = q_49_cast_fp16)[name = tensor("op_3779_cast_fp16")]; + tensor var_3780 = const()[name = tensor("op_3780"), val = tensor([2, 10, 64, -1])]; + tensor var_3781_cast_fp16 = reshape(shape = var_3780, x = k_49_cast_fp16)[name = tensor("op_3781_cast_fp16")]; + tensor var_3782 = const()[name = tensor("op_3782"), val = tensor([2, 10, 64, -1])]; + tensor var_3783_cast_fp16 = reshape(shape = var_3782, x = v_49_cast_fp16)[name = tensor("op_3783_cast_fp16")]; + tensor attn_weights_97_transpose_x_0 = const()[name = tensor("attn_weights_97_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_97_transpose_y_0 = const()[name = tensor("attn_weights_97_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_97_cast_fp16 = matmul(transpose_x = attn_weights_97_transpose_x_0, transpose_y = attn_weights_97_transpose_y_0, x = var_3779_cast_fp16, y = var_3781_cast_fp16)[name = tensor("attn_weights_97_cast_fp16")]; + tensor attn_weights_99_cast_fp16 = mul(x = attn_weights_97_cast_fp16, y = var_3117_to_fp16)[name = tensor("attn_weights_99_cast_fp16")]; + tensor var_3787_cast_fp16 = softmax(axis = var_3110, x = attn_weights_99_cast_fp16)[name = tensor("op_3787_cast_fp16")]; + tensor attn_49_transpose_x_0 = const()[name = tensor("attn_49_transpose_x_0"), val = tensor(false)]; + tensor attn_49_transpose_y_0 = const()[name = tensor("attn_49_transpose_y_0"), val = tensor(true)]; + tensor attn_49_cast_fp16 = matmul(transpose_x = attn_49_transpose_x_0, transpose_y = attn_49_transpose_y_0, x = var_3783_cast_fp16, y = var_3787_cast_fp16)[name = tensor("attn_49_cast_fp16")]; + tensor var_3791 = const()[name = tensor("op_3791"), val = tensor([2, 640, 1, -1])]; + tensor input_435_cast_fp16 = reshape(shape = var_3791, x = attn_49_cast_fp16)[name = tensor("input_435_cast_fp16")]; + tensor var_3796 = const()[name = tensor("op_3796"), val = tensor([1, 1])]; + tensor var_3798 = const()[name = tensor("op_3798"), val = tensor([1, 1])]; + tensor var_3800_pad_type_0 = const()[name = tensor("op_3800_pad_type_0"), val = tensor("custom")]; + tensor var_3800_pad_0 = const()[name = tensor("op_3800_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(776688768)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(777508032)))]; + tensor var_3800_cast_fp16 = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3798, groups = var_3126, pad = var_3800_pad_0, pad_type = var_3800_pad_type_0, strides = var_3796, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_435_cast_fp16)[name = tensor("op_3800_cast_fp16")]; + tensor inputs_75_cast_fp16 = add(x = var_3800_cast_fp16, y = inputs_73_cast_fp16)[name = tensor("inputs_75_cast_fp16")]; + tensor var_3804 = const()[name = tensor("op_3804"), val = tensor([1])]; + tensor channels_mean_75_cast_fp16 = reduce_mean(axes = var_3804, keep_dims = var_3121, x = inputs_75_cast_fp16)[name = tensor("channels_mean_75_cast_fp16")]; + tensor zero_mean_75_cast_fp16 = sub(x = inputs_75_cast_fp16, y = channels_mean_75_cast_fp16)[name = tensor("zero_mean_75_cast_fp16")]; + tensor zero_mean_sq_75_cast_fp16 = mul(x = zero_mean_75_cast_fp16, y = zero_mean_75_cast_fp16)[name = tensor("zero_mean_sq_75_cast_fp16")]; + tensor var_3808 = const()[name = tensor("op_3808"), val = tensor([1])]; + tensor var_3809_cast_fp16 = reduce_mean(axes = var_3808, keep_dims = var_3121, x = zero_mean_sq_75_cast_fp16)[name = tensor("op_3809_cast_fp16")]; + tensor var_3810_to_fp16 = const()[name = tensor("op_3810_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3811_cast_fp16 = add(x = var_3809_cast_fp16, y = var_3810_to_fp16)[name = tensor("op_3811_cast_fp16")]; + tensor denom_75_epsilon_0_to_fp16 = const()[name = tensor("denom_75_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_75_cast_fp16 = rsqrt(epsilon = denom_75_epsilon_0_to_fp16, x = var_3811_cast_fp16)[name = tensor("denom_75_cast_fp16")]; + tensor out_75_cast_fp16 = mul(x = zero_mean_75_cast_fp16, y = denom_75_cast_fp16)[name = tensor("out_75_cast_fp16")]; + tensor var_3815_to_fp16 = const()[name = tensor("op_3815_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(777509376)))]; + tensor var_3816_cast_fp16 = add(x = out_75_cast_fp16, y = var_3815_to_fp16)[name = tensor("op_3816_cast_fp16")]; + tensor var_3818_to_fp16 = const()[name = tensor("op_3818_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(777510720)))]; + tensor hidden_states_269_cast_fp16 = mul(x = var_3816_cast_fp16, y = var_3818_to_fp16)[name = tensor("hidden_states_269_cast_fp16")]; + tensor var_3825 = const()[name = tensor("op_3825"), val = tensor([1, 1])]; + tensor var_3827 = const()[name = tensor("op_3827"), val = tensor([1, 1])]; + tensor q_51_pad_type_0 = const()[name = tensor("q_51_pad_type_0"), val = tensor("custom")]; + tensor q_51_pad_0 = const()[name = tensor("q_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(777512064)))]; + tensor q_51_cast_fp16 = conv(dilations = var_3827, groups = var_3126, pad = q_51_pad_0, pad_type = q_51_pad_type_0, strides = var_3825, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_269_cast_fp16)[name = tensor("q_51_cast_fp16")]; + tensor var_3831 = const()[name = tensor("op_3831"), val = tensor([1, 1])]; + tensor var_3833 = const()[name = tensor("op_3833"), val = tensor([1, 1])]; + tensor k_51_pad_type_0 = const()[name = tensor("k_51_pad_type_0"), val = tensor("custom")]; + tensor k_51_pad_0 = const()[name = tensor("k_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(778331328)))]; + tensor k_51_cast_fp16 = conv(dilations = var_3833, groups = var_3126, pad = k_51_pad_0, pad_type = k_51_pad_type_0, strides = var_3831, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_51_cast_fp16")]; + tensor var_3837 = const()[name = tensor("op_3837"), val = tensor([1, 1])]; + tensor var_3839 = const()[name = tensor("op_3839"), val = tensor([1, 1])]; + tensor v_51_pad_type_0 = const()[name = tensor("v_51_pad_type_0"), val = tensor("custom")]; + tensor v_51_pad_0 = const()[name = tensor("v_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(779642112)))]; + tensor v_51_cast_fp16 = conv(dilations = var_3839, groups = var_3126, pad = v_51_pad_0, pad_type = v_51_pad_type_0, strides = var_3837, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_51_cast_fp16")]; + tensor var_3843 = const()[name = tensor("op_3843"), val = tensor([2, 10, 64, -1])]; + tensor var_3844_cast_fp16 = reshape(shape = var_3843, x = q_51_cast_fp16)[name = tensor("op_3844_cast_fp16")]; + tensor var_3845 = const()[name = tensor("op_3845"), val = tensor([2, 10, 64, -1])]; + tensor var_3846_cast_fp16 = reshape(shape = var_3845, x = k_51_cast_fp16)[name = tensor("op_3846_cast_fp16")]; + tensor var_3847 = const()[name = tensor("op_3847"), val = tensor([2, 10, 64, -1])]; + tensor var_3848_cast_fp16 = reshape(shape = var_3847, x = v_51_cast_fp16)[name = tensor("op_3848_cast_fp16")]; + tensor attn_weights_101_transpose_x_0 = const()[name = tensor("attn_weights_101_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_101_transpose_y_0 = const()[name = tensor("attn_weights_101_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_101_cast_fp16 = matmul(transpose_x = attn_weights_101_transpose_x_0, transpose_y = attn_weights_101_transpose_y_0, x = var_3844_cast_fp16, y = var_3846_cast_fp16)[name = tensor("attn_weights_101_cast_fp16")]; + tensor attn_weights_103_cast_fp16 = mul(x = attn_weights_101_cast_fp16, y = var_3117_to_fp16)[name = tensor("attn_weights_103_cast_fp16")]; + tensor var_3852_cast_fp16 = softmax(axis = var_3110, x = attn_weights_103_cast_fp16)[name = tensor("op_3852_cast_fp16")]; + tensor attn_51_transpose_x_0 = const()[name = tensor("attn_51_transpose_x_0"), val = tensor(false)]; + tensor attn_51_transpose_y_0 = const()[name = tensor("attn_51_transpose_y_0"), val = tensor(true)]; + tensor attn_51_cast_fp16 = matmul(transpose_x = attn_51_transpose_x_0, transpose_y = attn_51_transpose_y_0, x = var_3848_cast_fp16, y = var_3852_cast_fp16)[name = tensor("attn_51_cast_fp16")]; + tensor var_3856 = const()[name = tensor("op_3856"), val = tensor([2, 640, 1, -1])]; + tensor input_437_cast_fp16 = reshape(shape = var_3856, x = attn_51_cast_fp16)[name = tensor("input_437_cast_fp16")]; + tensor var_3861 = const()[name = tensor("op_3861"), val = tensor([1, 1])]; + tensor var_3863 = const()[name = tensor("op_3863"), val = tensor([1, 1])]; + tensor var_3865_pad_type_0 = const()[name = tensor("op_3865_pad_type_0"), val = tensor("custom")]; + tensor var_3865_pad_0 = const()[name = tensor("op_3865_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(780952896)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(781772160)))]; + tensor var_3865_cast_fp16 = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3863, groups = var_3126, pad = var_3865_pad_0, pad_type = var_3865_pad_type_0, strides = var_3861, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_437_cast_fp16)[name = tensor("op_3865_cast_fp16")]; + tensor inputs_77_cast_fp16 = add(x = var_3865_cast_fp16, y = inputs_75_cast_fp16)[name = tensor("inputs_77_cast_fp16")]; + tensor var_3869 = const()[name = tensor("op_3869"), val = tensor([1])]; + tensor channels_mean_77_cast_fp16 = reduce_mean(axes = var_3869, keep_dims = var_3121, x = inputs_77_cast_fp16)[name = tensor("channels_mean_77_cast_fp16")]; + tensor zero_mean_77_cast_fp16 = sub(x = inputs_77_cast_fp16, y = channels_mean_77_cast_fp16)[name = tensor("zero_mean_77_cast_fp16")]; + tensor zero_mean_sq_77_cast_fp16 = mul(x = zero_mean_77_cast_fp16, y = zero_mean_77_cast_fp16)[name = tensor("zero_mean_sq_77_cast_fp16")]; + tensor var_3873 = const()[name = tensor("op_3873"), val = tensor([1])]; + tensor var_3874_cast_fp16 = reduce_mean(axes = var_3873, keep_dims = var_3121, x = zero_mean_sq_77_cast_fp16)[name = tensor("op_3874_cast_fp16")]; + tensor var_3875_to_fp16 = const()[name = tensor("op_3875_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3876_cast_fp16 = add(x = var_3874_cast_fp16, y = var_3875_to_fp16)[name = tensor("op_3876_cast_fp16")]; + tensor denom_77_epsilon_0_to_fp16 = const()[name = tensor("denom_77_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_77_cast_fp16 = rsqrt(epsilon = denom_77_epsilon_0_to_fp16, x = var_3876_cast_fp16)[name = tensor("denom_77_cast_fp16")]; + tensor out_77_cast_fp16 = mul(x = zero_mean_77_cast_fp16, y = denom_77_cast_fp16)[name = tensor("out_77_cast_fp16")]; + tensor var_3880_to_fp16 = const()[name = tensor("op_3880_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(781773504)))]; + tensor var_3881_cast_fp16 = add(x = out_77_cast_fp16, y = var_3880_to_fp16)[name = tensor("op_3881_cast_fp16")]; + tensor var_3883_to_fp16 = const()[name = tensor("op_3883_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(781774848)))]; + tensor input_439_cast_fp16 = mul(x = var_3881_cast_fp16, y = var_3883_to_fp16)[name = tensor("input_439_cast_fp16")]; + tensor var_3891 = const()[name = tensor("op_3891"), val = tensor([1, 1])]; + tensor var_3893 = const()[name = tensor("op_3893"), val = tensor([1, 1])]; + tensor var_3895_pad_type_0 = const()[name = tensor("op_3895_pad_type_0"), val = tensor("custom")]; + tensor var_3895_pad_0 = const()[name = tensor("op_3895_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(781776192)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(788329856)))]; + tensor var_3895_cast_fp16 = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_3893, groups = var_3126, pad = var_3895_pad_0, pad_type = var_3895_pad_type_0, strides = var_3891, weight = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_439_cast_fp16)[name = tensor("op_3895_cast_fp16")]; + tensor var_3896_split_sizes_0 = const()[name = tensor("op_3896_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_3896_axis_0 = const()[name = tensor("op_3896_axis_0"), val = tensor(1)]; + tensor var_3896_cast_fp16_0, tensor var_3896_cast_fp16_1 = split(axis = var_3896_axis_0, split_sizes = var_3896_split_sizes_0, x = var_3895_cast_fp16)[name = tensor("op_3896_cast_fp16")]; + tensor var_3898_mode_0 = const()[name = tensor("op_3898_mode_0"), val = tensor("EXACT")]; + tensor var_3898_cast_fp16 = gelu(mode = var_3898_mode_0, x = var_3896_cast_fp16_1)[name = tensor("op_3898_cast_fp16")]; + tensor input_441_cast_fp16 = mul(x = var_3896_cast_fp16_0, y = var_3898_cast_fp16)[name = tensor("input_441_cast_fp16")]; + tensor var_3902 = const()[name = tensor("op_3902"), val = tensor([1, 1])]; + tensor var_3904 = const()[name = tensor("op_3904"), val = tensor([1, 1])]; + tensor var_3906_pad_type_0 = const()[name = tensor("op_3906_pad_type_0"), val = tensor("custom")]; + tensor var_3906_pad_0 = const()[name = tensor("op_3906_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(788340160)))]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(791617024)))]; + tensor var_3906_cast_fp16 = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3904, groups = var_3126, pad = var_3906_pad_0, pad_type = var_3906_pad_type_0, strides = var_3902, weight = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_441_cast_fp16)[name = tensor("op_3906_cast_fp16")]; + tensor hidden_states_273_cast_fp16 = add(x = var_3906_cast_fp16, y = inputs_77_cast_fp16)[name = tensor("hidden_states_273_cast_fp16")]; + tensor var_3908 = const()[name = tensor("op_3908"), val = tensor([2, 640, 24, 40])]; + tensor input_443_cast_fp16 = reshape(shape = var_3908, x = hidden_states_273_cast_fp16)[name = tensor("input_443_cast_fp16")]; + tensor var_3912 = const()[name = tensor("op_3912"), val = tensor([1, 1])]; + tensor var_3914 = const()[name = tensor("op_3914"), val = tensor([1, 1])]; + tensor hidden_states_275_pad_type_0 = const()[name = tensor("hidden_states_275_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_275_pad_0 = const()[name = tensor("hidden_states_275_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(791618368)))]; + tensor up_blocks_2_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(792437632)))]; + tensor hidden_states_275_cast_fp16 = conv(bias = up_blocks_2_attentions_2_proj_out_bias_to_fp16, dilations = var_3914, groups = var_3126, pad = hidden_states_275_pad_0, pad_type = hidden_states_275_pad_type_0, strides = var_3912, weight = up_blocks_2_attentions_2_proj_out_weight_to_fp16, x = input_443_cast_fp16)[name = tensor("hidden_states_275_cast_fp16")]; + tensor input_445_cast_fp16 = add(x = hidden_states_275_cast_fp16, y = hidden_states_263_cast_fp16)[name = tensor("input_445_cast_fp16")]; + tensor input_447_scale_factor_height_0 = const()[name = tensor("input_447_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_447_scale_factor_width_0 = const()[name = tensor("input_447_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_447_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = input_447_scale_factor_height_0, scale_factor_width = input_447_scale_factor_width_0, x = input_445_cast_fp16)[name = tensor("input_447_cast_fp16")]; + tensor var_3923 = const()[name = tensor("op_3923"), val = tensor([1, 1])]; + tensor var_3925 = const()[name = tensor("op_3925"), val = tensor([1, 1])]; + tensor hidden_states_277_pad_type_0 = const()[name = tensor("hidden_states_277_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_277_pad_0 = const()[name = tensor("hidden_states_277_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("up_blocks_2_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(792438976)))]; + tensor up_blocks_2_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_2_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(799811840)))]; + tensor hidden_states_277_cast_fp16 = conv(bias = up_blocks_2_upsamplers_0_conv_bias_to_fp16, dilations = var_3925, groups = var_3126, pad = hidden_states_277_pad_0, pad_type = hidden_states_277_pad_type_0, strides = var_3923, weight = up_blocks_2_upsamplers_0_conv_weight_to_fp16, x = input_447_cast_fp16)[name = tensor("hidden_states_277_cast_fp16")]; + tensor var_3929 = const()[name = tensor("op_3929"), val = tensor(3)]; + tensor var_3940 = const()[name = tensor("op_3940"), val = tensor(true)]; + tensor var_3945 = const()[name = tensor("op_3945"), val = tensor(1)]; + tensor input_449_interleave_0 = const()[name = tensor("input_449_interleave_0"), val = tensor(false)]; + tensor cast_10 = cast(dtype = cast_9_dtype_0, x = input_61_cast_fp16)[name = tensor("cast_10")]; + tensor input_449_cast_fp16 = concat(axis = var_3945, interleave = input_449_interleave_0, values = (hidden_states_277_cast_fp16, cast_10))[name = tensor("input_449_cast_fp16")]; + tensor reshape_204_shape_0 = const()[name = tensor("reshape_204_shape_0"), val = tensor([2, 32, 30, 48, 80])]; + tensor reshape_204_cast_fp16 = reshape(shape = reshape_204_shape_0, x = input_449_cast_fp16)[name = tensor("reshape_204_cast_fp16")]; + tensor reduce_mean_153_axes_0 = const()[name = tensor("reduce_mean_153_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_153_keep_dims_0 = const()[name = tensor("reduce_mean_153_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_153_cast_fp16 = reduce_mean(axes = reduce_mean_153_axes_0, keep_dims = reduce_mean_153_keep_dims_0, x = reshape_204_cast_fp16)[name = tensor("reduce_mean_153_cast_fp16")]; + tensor sub_102_cast_fp16 = sub(x = reshape_204_cast_fp16, y = reduce_mean_153_cast_fp16)[name = tensor("sub_102_cast_fp16")]; + tensor square_51_cast_fp16 = square(x = sub_102_cast_fp16)[name = tensor("square_51_cast_fp16")]; + tensor reduce_mean_155_axes_0 = const()[name = tensor("reduce_mean_155_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_155_keep_dims_0 = const()[name = tensor("reduce_mean_155_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_155_cast_fp16 = reduce_mean(axes = reduce_mean_155_axes_0, keep_dims = reduce_mean_155_keep_dims_0, x = square_51_cast_fp16)[name = tensor("reduce_mean_155_cast_fp16")]; + tensor add_102_y_0_to_fp16 = const()[name = tensor("add_102_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_102_cast_fp16 = add(x = reduce_mean_155_cast_fp16, y = add_102_y_0_to_fp16)[name = tensor("add_102_cast_fp16")]; + tensor sqrt_51_cast_fp16 = sqrt(x = add_102_cast_fp16)[name = tensor("sqrt_51_cast_fp16")]; + tensor real_div_51_cast_fp16 = real_div(x = sub_102_cast_fp16, y = sqrt_51_cast_fp16)[name = tensor("real_div_51_cast_fp16")]; + tensor reshape_205_shape_0 = const()[name = tensor("reshape_205_shape_0"), val = tensor([2, 960, 48, 80])]; + tensor reshape_205_cast_fp16 = reshape(shape = reshape_205_shape_0, x = real_div_51_cast_fp16)[name = tensor("reshape_205_cast_fp16")]; + tensor add_103_gamma_0_to_fp16 = const()[name = tensor("add_103_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(799813184)))]; + tensor add_103_beta_0_to_fp16 = const()[name = tensor("add_103_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(799815168)))]; + tensor add_103_epsilon_0_to_fp16 = const()[name = tensor("add_103_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_103_cast_fp16 = batch_norm(beta = add_103_beta_0_to_fp16, epsilon = add_103_epsilon_0_to_fp16, gamma = add_103_gamma_0_to_fp16, mean = add_97_mean_0_to_fp16, variance = add_97_variance_0_to_fp16, x = reshape_205_cast_fp16)[name = tensor("add_103_cast_fp16")]; + tensor input_453_cast_fp16 = silu(x = add_103_cast_fp16)[name = tensor("input_453_cast_fp16")]; + tensor var_3972 = const()[name = tensor("op_3972"), val = tensor([1, 1])]; + tensor var_3974 = const()[name = tensor("op_3974"), val = tensor([1, 1])]; + tensor hidden_states_279_pad_type_0 = const()[name = tensor("hidden_states_279_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_279_pad_0 = const()[name = tensor("hidden_states_279_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(799817152)))]; + tensor up_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(805346816)))]; + tensor hidden_states_279_cast_fp16 = conv(bias = up_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = var_3974, groups = var_3945, pad = hidden_states_279_pad_0, pad_type = hidden_states_279_pad_type_0, strides = var_3972, weight = up_blocks_3_resnets_0_conv1_weight_to_fp16, x = input_453_cast_fp16)[name = tensor("hidden_states_279_cast_fp16")]; + tensor var_3980 = const()[name = tensor("op_3980"), val = tensor([1, 1])]; + tensor var_3982 = const()[name = tensor("op_3982"), val = tensor([1, 1])]; + tensor temb_39_pad_type_0 = const()[name = tensor("temb_39_pad_type_0"), val = tensor("custom")]; + tensor temb_39_pad_0 = const()[name = tensor("temb_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(805347520)))]; + tensor up_blocks_3_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(806166784)))]; + tensor temb_39_cast_fp16 = conv(bias = up_blocks_3_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_3982, groups = var_3945, pad = temb_39_pad_0, pad_type = temb_39_pad_type_0, strides = var_3980, weight = up_blocks_3_resnets_0_time_emb_proj_weight_to_fp16, x = cast_12)[name = tensor("temb_39_cast_fp16")]; + tensor input_457_cast_fp16 = add(x = hidden_states_279_cast_fp16, y = temb_39_cast_fp16)[name = tensor("input_457_cast_fp16")]; + tensor reshape_208_shape_0 = const()[name = tensor("reshape_208_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_208_cast_fp16 = reshape(shape = reshape_208_shape_0, x = input_457_cast_fp16)[name = tensor("reshape_208_cast_fp16")]; + tensor reduce_mean_156_axes_0 = const()[name = tensor("reduce_mean_156_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_156_keep_dims_0 = const()[name = tensor("reduce_mean_156_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_156_cast_fp16 = reduce_mean(axes = reduce_mean_156_axes_0, keep_dims = reduce_mean_156_keep_dims_0, x = reshape_208_cast_fp16)[name = tensor("reduce_mean_156_cast_fp16")]; + tensor sub_104_cast_fp16 = sub(x = reshape_208_cast_fp16, y = reduce_mean_156_cast_fp16)[name = tensor("sub_104_cast_fp16")]; + tensor square_52_cast_fp16 = square(x = sub_104_cast_fp16)[name = tensor("square_52_cast_fp16")]; + tensor reduce_mean_158_axes_0 = const()[name = tensor("reduce_mean_158_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_158_keep_dims_0 = const()[name = tensor("reduce_mean_158_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_158_cast_fp16 = reduce_mean(axes = reduce_mean_158_axes_0, keep_dims = reduce_mean_158_keep_dims_0, x = square_52_cast_fp16)[name = tensor("reduce_mean_158_cast_fp16")]; + tensor add_104_y_0_to_fp16 = const()[name = tensor("add_104_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_104_cast_fp16 = add(x = reduce_mean_158_cast_fp16, y = add_104_y_0_to_fp16)[name = tensor("add_104_cast_fp16")]; + tensor sqrt_52_cast_fp16 = sqrt(x = add_104_cast_fp16)[name = tensor("sqrt_52_cast_fp16")]; + tensor real_div_52_cast_fp16 = real_div(x = sub_104_cast_fp16, y = sqrt_52_cast_fp16)[name = tensor("real_div_52_cast_fp16")]; + tensor reshape_209_shape_0 = const()[name = tensor("reshape_209_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_209_cast_fp16 = reshape(shape = reshape_209_shape_0, x = real_div_52_cast_fp16)[name = tensor("reshape_209_cast_fp16")]; + tensor add_105_gamma_0_to_fp16 = const()[name = tensor("add_105_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(806167488)))]; + tensor add_105_beta_0_to_fp16 = const()[name = tensor("add_105_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(806168192)))]; + tensor add_105_epsilon_0_to_fp16 = const()[name = tensor("add_105_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_105_cast_fp16 = batch_norm(beta = add_105_beta_0_to_fp16, epsilon = add_105_epsilon_0_to_fp16, gamma = add_105_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_209_cast_fp16)[name = tensor("add_105_cast_fp16")]; + tensor input_461_cast_fp16 = silu(x = add_105_cast_fp16)[name = tensor("input_461_cast_fp16")]; + tensor var_3992 = const()[name = tensor("op_3992"), val = tensor([1, 1])]; + tensor var_3994 = const()[name = tensor("op_3994"), val = tensor([1, 1])]; + tensor hidden_states_281_pad_type_0 = const()[name = tensor("hidden_states_281_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_281_pad_0 = const()[name = tensor("hidden_states_281_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(806168896)))]; + tensor up_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808012160)))]; + tensor hidden_states_281_cast_fp16 = conv(bias = up_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = var_3994, groups = var_3945, pad = hidden_states_281_pad_0, pad_type = hidden_states_281_pad_type_0, strides = var_3992, weight = up_blocks_3_resnets_0_conv2_weight_to_fp16, x = input_461_cast_fp16)[name = tensor("hidden_states_281_cast_fp16")]; + tensor var_3999 = const()[name = tensor("op_3999"), val = tensor([1, 1])]; + tensor var_4001 = const()[name = tensor("op_4001"), val = tensor([1, 1])]; + tensor x_23_pad_type_0 = const()[name = tensor("x_23_pad_type_0"), val = tensor("custom")]; + tensor x_23_pad_0 = const()[name = tensor("x_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808012864)))]; + tensor up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808627328)))]; + tensor x_23_cast_fp16 = conv(bias = up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_4001, groups = var_3945, pad = x_23_pad_0, pad_type = x_23_pad_type_0, strides = var_3999, weight = up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16, x = input_449_cast_fp16)[name = tensor("x_23_cast_fp16")]; + tensor hidden_states_283_cast_fp16 = add(x = x_23_cast_fp16, y = hidden_states_281_cast_fp16)[name = tensor("hidden_states_283_cast_fp16")]; + tensor reshape_212_shape_0 = const()[name = tensor("reshape_212_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_212_cast_fp16 = reshape(shape = reshape_212_shape_0, x = hidden_states_283_cast_fp16)[name = tensor("reshape_212_cast_fp16")]; + tensor reduce_mean_159_axes_0 = const()[name = tensor("reduce_mean_159_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_159_keep_dims_0 = const()[name = tensor("reduce_mean_159_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_159_cast_fp16 = reduce_mean(axes = reduce_mean_159_axes_0, keep_dims = reduce_mean_159_keep_dims_0, x = reshape_212_cast_fp16)[name = tensor("reduce_mean_159_cast_fp16")]; + tensor sub_106_cast_fp16 = sub(x = reshape_212_cast_fp16, y = reduce_mean_159_cast_fp16)[name = tensor("sub_106_cast_fp16")]; + tensor square_53_cast_fp16 = square(x = sub_106_cast_fp16)[name = tensor("square_53_cast_fp16")]; + tensor reduce_mean_161_axes_0 = const()[name = tensor("reduce_mean_161_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_161_keep_dims_0 = const()[name = tensor("reduce_mean_161_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_161_cast_fp16 = reduce_mean(axes = reduce_mean_161_axes_0, keep_dims = reduce_mean_161_keep_dims_0, x = square_53_cast_fp16)[name = tensor("reduce_mean_161_cast_fp16")]; + tensor add_106_y_0_to_fp16 = const()[name = tensor("add_106_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_106_cast_fp16 = add(x = reduce_mean_161_cast_fp16, y = add_106_y_0_to_fp16)[name = tensor("add_106_cast_fp16")]; + tensor sqrt_53_cast_fp16 = sqrt(x = add_106_cast_fp16)[name = tensor("sqrt_53_cast_fp16")]; + tensor real_div_53_cast_fp16 = real_div(x = sub_106_cast_fp16, y = sqrt_53_cast_fp16)[name = tensor("real_div_53_cast_fp16")]; + tensor reshape_213_shape_0 = const()[name = tensor("reshape_213_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_213_cast_fp16 = reshape(shape = reshape_213_shape_0, x = real_div_53_cast_fp16)[name = tensor("reshape_213_cast_fp16")]; + tensor add_107_gamma_0_to_fp16 = const()[name = tensor("add_107_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808628032)))]; + tensor add_107_beta_0_to_fp16 = const()[name = tensor("add_107_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808628736)))]; + tensor add_107_epsilon_0_to_fp16 = const()[name = tensor("add_107_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_107_cast_fp16 = batch_norm(beta = add_107_beta_0_to_fp16, epsilon = add_107_epsilon_0_to_fp16, gamma = add_107_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_213_cast_fp16)[name = tensor("add_107_cast_fp16")]; + tensor var_4021 = const()[name = tensor("op_4021"), val = tensor([1, 1])]; + tensor var_4023 = const()[name = tensor("op_4023"), val = tensor([1, 1])]; + tensor hidden_states_285_pad_type_0 = const()[name = tensor("hidden_states_285_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_285_pad_0 = const()[name = tensor("hidden_states_285_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808629440)))]; + tensor up_blocks_3_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808834304)))]; + tensor hidden_states_285_cast_fp16 = conv(bias = up_blocks_3_attentions_0_proj_in_bias_to_fp16, dilations = var_4023, groups = var_3945, pad = hidden_states_285_pad_0, pad_type = hidden_states_285_pad_type_0, strides = var_4021, weight = up_blocks_3_attentions_0_proj_in_weight_to_fp16, x = add_107_cast_fp16)[name = tensor("hidden_states_285_cast_fp16")]; + tensor var_4028 = const()[name = tensor("op_4028"), val = tensor([2, 320, 1, 3840])]; + tensor inputs_79_cast_fp16 = reshape(shape = var_4028, x = hidden_states_285_cast_fp16)[name = tensor("inputs_79_cast_fp16")]; + tensor var_4038 = const()[name = tensor("op_4038"), val = tensor([1])]; + tensor channels_mean_79_cast_fp16 = reduce_mean(axes = var_4038, keep_dims = var_3940, x = inputs_79_cast_fp16)[name = tensor("channels_mean_79_cast_fp16")]; + tensor zero_mean_79_cast_fp16 = sub(x = inputs_79_cast_fp16, y = channels_mean_79_cast_fp16)[name = tensor("zero_mean_79_cast_fp16")]; + tensor zero_mean_sq_79_cast_fp16 = mul(x = zero_mean_79_cast_fp16, y = zero_mean_79_cast_fp16)[name = tensor("zero_mean_sq_79_cast_fp16")]; + tensor var_4042 = const()[name = tensor("op_4042"), val = tensor([1])]; + tensor var_4043_cast_fp16 = reduce_mean(axes = var_4042, keep_dims = var_3940, x = zero_mean_sq_79_cast_fp16)[name = tensor("op_4043_cast_fp16")]; + tensor var_4044_to_fp16 = const()[name = tensor("op_4044_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4045_cast_fp16 = add(x = var_4043_cast_fp16, y = var_4044_to_fp16)[name = tensor("op_4045_cast_fp16")]; + tensor denom_79_epsilon_0_to_fp16 = const()[name = tensor("denom_79_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_79_cast_fp16 = rsqrt(epsilon = denom_79_epsilon_0_to_fp16, x = var_4045_cast_fp16)[name = tensor("denom_79_cast_fp16")]; + tensor out_79_cast_fp16 = mul(x = zero_mean_79_cast_fp16, y = denom_79_cast_fp16)[name = tensor("out_79_cast_fp16")]; + tensor var_4049_to_fp16 = const()[name = tensor("op_4049_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808835008)))]; + tensor var_4050_cast_fp16 = add(x = out_79_cast_fp16, y = var_4049_to_fp16)[name = tensor("op_4050_cast_fp16")]; + tensor var_4052_to_fp16 = const()[name = tensor("op_4052_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808835712)))]; + tensor hidden_states_287_cast_fp16 = mul(x = var_4050_cast_fp16, y = var_4052_to_fp16)[name = tensor("hidden_states_287_cast_fp16")]; + tensor var_4059 = const()[name = tensor("op_4059"), val = tensor([1, 1])]; + tensor var_4061 = const()[name = tensor("op_4061"), val = tensor([1, 1])]; + tensor q_53_pad_type_0 = const()[name = tensor("q_53_pad_type_0"), val = tensor("custom")]; + tensor q_53_pad_0 = const()[name = tensor("q_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(808836416)))]; + tensor q_53_cast_fp16 = conv(dilations = var_4061, groups = var_3945, pad = q_53_pad_0, pad_type = q_53_pad_type_0, strides = var_4059, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_287_cast_fp16)[name = tensor("q_53_cast_fp16")]; + tensor var_4065 = const()[name = tensor("op_4065"), val = tensor([1, 1])]; + tensor var_4067 = const()[name = tensor("op_4067"), val = tensor([1, 1])]; + tensor k_53_pad_type_0 = const()[name = tensor("k_53_pad_type_0"), val = tensor("custom")]; + tensor k_53_pad_0 = const()[name = tensor("k_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809041280)))]; + tensor k_53_cast_fp16 = conv(dilations = var_4067, groups = var_3945, pad = k_53_pad_0, pad_type = k_53_pad_type_0, strides = var_4065, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_287_cast_fp16)[name = tensor("k_53_cast_fp16")]; + tensor var_4071 = const()[name = tensor("op_4071"), val = tensor([1, 1])]; + tensor var_4073 = const()[name = tensor("op_4073"), val = tensor([1, 1])]; + tensor v_53_pad_type_0 = const()[name = tensor("v_53_pad_type_0"), val = tensor("custom")]; + tensor v_53_pad_0 = const()[name = tensor("v_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809246144)))]; + tensor v_53_cast_fp16 = conv(dilations = var_4073, groups = var_3945, pad = v_53_pad_0, pad_type = v_53_pad_type_0, strides = var_4071, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_287_cast_fp16)[name = tensor("v_53_cast_fp16")]; + tensor var_4077 = const()[name = tensor("op_4077"), val = tensor([2, 5, 64, -1])]; + tensor var_4078_cast_fp16 = reshape(shape = var_4077, x = q_53_cast_fp16)[name = tensor("op_4078_cast_fp16")]; + tensor var_4079 = const()[name = tensor("op_4079"), val = tensor([2, 5, 64, -1])]; + tensor var_4080_cast_fp16 = reshape(shape = var_4079, x = k_53_cast_fp16)[name = tensor("op_4080_cast_fp16")]; + tensor var_4081 = const()[name = tensor("op_4081"), val = tensor([2, 5, 64, -1])]; + tensor var_4082_cast_fp16 = reshape(shape = var_4081, x = v_53_cast_fp16)[name = tensor("op_4082_cast_fp16")]; + tensor attn_weights_105_transpose_x_0 = const()[name = tensor("attn_weights_105_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_105_transpose_y_0 = const()[name = tensor("attn_weights_105_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_105_cast_fp16 = matmul(transpose_x = attn_weights_105_transpose_x_0, transpose_y = attn_weights_105_transpose_y_0, x = var_4078_cast_fp16, y = var_4080_cast_fp16)[name = tensor("attn_weights_105_cast_fp16")]; + tensor var_3936_to_fp16 = const()[name = tensor("op_3936_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_107_cast_fp16 = mul(x = attn_weights_105_cast_fp16, y = var_3936_to_fp16)[name = tensor("attn_weights_107_cast_fp16")]; + tensor var_4086_cast_fp16 = softmax(axis = var_3929, x = attn_weights_107_cast_fp16)[name = tensor("op_4086_cast_fp16")]; + tensor attn_53_transpose_x_0 = const()[name = tensor("attn_53_transpose_x_0"), val = tensor(false)]; + tensor attn_53_transpose_y_0 = const()[name = tensor("attn_53_transpose_y_0"), val = tensor(true)]; + tensor attn_53_cast_fp16 = matmul(transpose_x = attn_53_transpose_x_0, transpose_y = attn_53_transpose_y_0, x = var_4082_cast_fp16, y = var_4086_cast_fp16)[name = tensor("attn_53_cast_fp16")]; + tensor var_4090 = const()[name = tensor("op_4090"), val = tensor([2, 320, 1, -1])]; + tensor input_465_cast_fp16 = reshape(shape = var_4090, x = attn_53_cast_fp16)[name = tensor("input_465_cast_fp16")]; + tensor var_4095 = const()[name = tensor("op_4095"), val = tensor([1, 1])]; + tensor var_4097 = const()[name = tensor("op_4097"), val = tensor([1, 1])]; + tensor var_4099_pad_type_0 = const()[name = tensor("op_4099_pad_type_0"), val = tensor("custom")]; + tensor var_4099_pad_0 = const()[name = tensor("op_4099_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809451008)))]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809655872)))]; + tensor var_4099_cast_fp16 = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_4097, groups = var_3945, pad = var_4099_pad_0, pad_type = var_4099_pad_type_0, strides = var_4095, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_465_cast_fp16)[name = tensor("op_4099_cast_fp16")]; + tensor inputs_81_cast_fp16 = add(x = var_4099_cast_fp16, y = inputs_79_cast_fp16)[name = tensor("inputs_81_cast_fp16")]; + tensor var_4103 = const()[name = tensor("op_4103"), val = tensor([1])]; + tensor channels_mean_81_cast_fp16 = reduce_mean(axes = var_4103, keep_dims = var_3940, x = inputs_81_cast_fp16)[name = tensor("channels_mean_81_cast_fp16")]; + tensor zero_mean_81_cast_fp16 = sub(x = inputs_81_cast_fp16, y = channels_mean_81_cast_fp16)[name = tensor("zero_mean_81_cast_fp16")]; + tensor zero_mean_sq_81_cast_fp16 = mul(x = zero_mean_81_cast_fp16, y = zero_mean_81_cast_fp16)[name = tensor("zero_mean_sq_81_cast_fp16")]; + tensor var_4107 = const()[name = tensor("op_4107"), val = tensor([1])]; + tensor var_4108_cast_fp16 = reduce_mean(axes = var_4107, keep_dims = var_3940, x = zero_mean_sq_81_cast_fp16)[name = tensor("op_4108_cast_fp16")]; + tensor var_4109_to_fp16 = const()[name = tensor("op_4109_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4110_cast_fp16 = add(x = var_4108_cast_fp16, y = var_4109_to_fp16)[name = tensor("op_4110_cast_fp16")]; + tensor denom_81_epsilon_0_to_fp16 = const()[name = tensor("denom_81_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_81_cast_fp16 = rsqrt(epsilon = denom_81_epsilon_0_to_fp16, x = var_4110_cast_fp16)[name = tensor("denom_81_cast_fp16")]; + tensor out_81_cast_fp16 = mul(x = zero_mean_81_cast_fp16, y = denom_81_cast_fp16)[name = tensor("out_81_cast_fp16")]; + tensor var_4114_to_fp16 = const()[name = tensor("op_4114_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809656576)))]; + tensor var_4115_cast_fp16 = add(x = out_81_cast_fp16, y = var_4114_to_fp16)[name = tensor("op_4115_cast_fp16")]; + tensor var_4117_to_fp16 = const()[name = tensor("op_4117_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809657280)))]; + tensor hidden_states_289_cast_fp16 = mul(x = var_4115_cast_fp16, y = var_4117_to_fp16)[name = tensor("hidden_states_289_cast_fp16")]; + tensor var_4124 = const()[name = tensor("op_4124"), val = tensor([1, 1])]; + tensor var_4126 = const()[name = tensor("op_4126"), val = tensor([1, 1])]; + tensor q_55_pad_type_0 = const()[name = tensor("q_55_pad_type_0"), val = tensor("custom")]; + tensor q_55_pad_0 = const()[name = tensor("q_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809657984)))]; + tensor q_55_cast_fp16 = conv(dilations = var_4126, groups = var_3945, pad = q_55_pad_0, pad_type = q_55_pad_type_0, strides = var_4124, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_289_cast_fp16)[name = tensor("q_55_cast_fp16")]; + tensor var_4130 = const()[name = tensor("op_4130"), val = tensor([1, 1])]; + tensor var_4132 = const()[name = tensor("op_4132"), val = tensor([1, 1])]; + tensor k_55_pad_type_0 = const()[name = tensor("k_55_pad_type_0"), val = tensor("custom")]; + tensor k_55_pad_0 = const()[name = tensor("k_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(809862848)))]; + tensor k_55_cast_fp16 = conv(dilations = var_4132, groups = var_3945, pad = k_55_pad_0, pad_type = k_55_pad_type_0, strides = var_4130, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_55_cast_fp16")]; + tensor var_4136 = const()[name = tensor("op_4136"), val = tensor([1, 1])]; + tensor var_4138 = const()[name = tensor("op_4138"), val = tensor([1, 1])]; + tensor v_55_pad_type_0 = const()[name = tensor("v_55_pad_type_0"), val = tensor("custom")]; + tensor v_55_pad_0 = const()[name = tensor("v_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(810518272)))]; + tensor v_55_cast_fp16 = conv(dilations = var_4138, groups = var_3945, pad = v_55_pad_0, pad_type = v_55_pad_type_0, strides = var_4136, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_55_cast_fp16")]; + tensor var_4142 = const()[name = tensor("op_4142"), val = tensor([2, 5, 64, -1])]; + tensor var_4143_cast_fp16 = reshape(shape = var_4142, x = q_55_cast_fp16)[name = tensor("op_4143_cast_fp16")]; + tensor var_4144 = const()[name = tensor("op_4144"), val = tensor([2, 5, 64, -1])]; + tensor var_4145_cast_fp16 = reshape(shape = var_4144, x = k_55_cast_fp16)[name = tensor("op_4145_cast_fp16")]; + tensor var_4146 = const()[name = tensor("op_4146"), val = tensor([2, 5, 64, -1])]; + tensor var_4147_cast_fp16 = reshape(shape = var_4146, x = v_55_cast_fp16)[name = tensor("op_4147_cast_fp16")]; + tensor attn_weights_109_transpose_x_0 = const()[name = tensor("attn_weights_109_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_109_transpose_y_0 = const()[name = tensor("attn_weights_109_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_109_cast_fp16 = matmul(transpose_x = attn_weights_109_transpose_x_0, transpose_y = attn_weights_109_transpose_y_0, x = var_4143_cast_fp16, y = var_4145_cast_fp16)[name = tensor("attn_weights_109_cast_fp16")]; + tensor attn_weights_111_cast_fp16 = mul(x = attn_weights_109_cast_fp16, y = var_3936_to_fp16)[name = tensor("attn_weights_111_cast_fp16")]; + tensor var_4151_cast_fp16 = softmax(axis = var_3929, x = attn_weights_111_cast_fp16)[name = tensor("op_4151_cast_fp16")]; + tensor attn_55_transpose_x_0 = const()[name = tensor("attn_55_transpose_x_0"), val = tensor(false)]; + tensor attn_55_transpose_y_0 = const()[name = tensor("attn_55_transpose_y_0"), val = tensor(true)]; + tensor attn_55_cast_fp16 = matmul(transpose_x = attn_55_transpose_x_0, transpose_y = attn_55_transpose_y_0, x = var_4147_cast_fp16, y = var_4151_cast_fp16)[name = tensor("attn_55_cast_fp16")]; + tensor var_4155 = const()[name = tensor("op_4155"), val = tensor([2, 320, 1, -1])]; + tensor input_467_cast_fp16 = reshape(shape = var_4155, x = attn_55_cast_fp16)[name = tensor("input_467_cast_fp16")]; + tensor var_4160 = const()[name = tensor("op_4160"), val = tensor([1, 1])]; + tensor var_4162 = const()[name = tensor("op_4162"), val = tensor([1, 1])]; + tensor var_4164_pad_type_0 = const()[name = tensor("op_4164_pad_type_0"), val = tensor("custom")]; + tensor var_4164_pad_0 = const()[name = tensor("op_4164_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(811173696)))]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(811378560)))]; + tensor var_4164_cast_fp16 = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_4162, groups = var_3945, pad = var_4164_pad_0, pad_type = var_4164_pad_type_0, strides = var_4160, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_467_cast_fp16)[name = tensor("op_4164_cast_fp16")]; + tensor inputs_83_cast_fp16 = add(x = var_4164_cast_fp16, y = inputs_81_cast_fp16)[name = tensor("inputs_83_cast_fp16")]; + tensor var_4168 = const()[name = tensor("op_4168"), val = tensor([1])]; + tensor channels_mean_83_cast_fp16 = reduce_mean(axes = var_4168, keep_dims = var_3940, x = inputs_83_cast_fp16)[name = tensor("channels_mean_83_cast_fp16")]; + tensor zero_mean_83_cast_fp16 = sub(x = inputs_83_cast_fp16, y = channels_mean_83_cast_fp16)[name = tensor("zero_mean_83_cast_fp16")]; + tensor zero_mean_sq_83_cast_fp16 = mul(x = zero_mean_83_cast_fp16, y = zero_mean_83_cast_fp16)[name = tensor("zero_mean_sq_83_cast_fp16")]; + tensor var_4172 = const()[name = tensor("op_4172"), val = tensor([1])]; + tensor var_4173_cast_fp16 = reduce_mean(axes = var_4172, keep_dims = var_3940, x = zero_mean_sq_83_cast_fp16)[name = tensor("op_4173_cast_fp16")]; + tensor var_4174_to_fp16 = const()[name = tensor("op_4174_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4175_cast_fp16 = add(x = var_4173_cast_fp16, y = var_4174_to_fp16)[name = tensor("op_4175_cast_fp16")]; + tensor denom_83_epsilon_0_to_fp16 = const()[name = tensor("denom_83_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_83_cast_fp16 = rsqrt(epsilon = denom_83_epsilon_0_to_fp16, x = var_4175_cast_fp16)[name = tensor("denom_83_cast_fp16")]; + tensor out_83_cast_fp16 = mul(x = zero_mean_83_cast_fp16, y = denom_83_cast_fp16)[name = tensor("out_83_cast_fp16")]; + tensor var_4179_to_fp16 = const()[name = tensor("op_4179_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(811379264)))]; + tensor var_4180_cast_fp16 = add(x = out_83_cast_fp16, y = var_4179_to_fp16)[name = tensor("op_4180_cast_fp16")]; + tensor var_4182_to_fp16 = const()[name = tensor("op_4182_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(811379968)))]; + tensor input_469_cast_fp16 = mul(x = var_4180_cast_fp16, y = var_4182_to_fp16)[name = tensor("input_469_cast_fp16")]; + tensor var_4190 = const()[name = tensor("op_4190"), val = tensor([1, 1])]; + tensor var_4192 = const()[name = tensor("op_4192"), val = tensor([1, 1])]; + tensor var_4194_pad_type_0 = const()[name = tensor("op_4194_pad_type_0"), val = tensor("custom")]; + tensor var_4194_pad_0 = const()[name = tensor("op_4194_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(811380672)))]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(813019136)))]; + tensor var_4194_cast_fp16 = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_4192, groups = var_3945, pad = var_4194_pad_0, pad_type = var_4194_pad_type_0, strides = var_4190, weight = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_469_cast_fp16)[name = tensor("op_4194_cast_fp16")]; + tensor var_4195_split_sizes_0 = const()[name = tensor("op_4195_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_4195_axis_0 = const()[name = tensor("op_4195_axis_0"), val = tensor(1)]; + tensor var_4195_cast_fp16_0, tensor var_4195_cast_fp16_1 = split(axis = var_4195_axis_0, split_sizes = var_4195_split_sizes_0, x = var_4194_cast_fp16)[name = tensor("op_4195_cast_fp16")]; + tensor var_4197_mode_0 = const()[name = tensor("op_4197_mode_0"), val = tensor("EXACT")]; + tensor var_4197_cast_fp16 = gelu(mode = var_4197_mode_0, x = var_4195_cast_fp16_1)[name = tensor("op_4197_cast_fp16")]; + tensor input_471_cast_fp16 = mul(x = var_4195_cast_fp16_0, y = var_4197_cast_fp16)[name = tensor("input_471_cast_fp16")]; + tensor var_4201 = const()[name = tensor("op_4201"), val = tensor([1, 1])]; + tensor var_4203 = const()[name = tensor("op_4203"), val = tensor([1, 1])]; + tensor var_4205_pad_type_0 = const()[name = tensor("op_4205_pad_type_0"), val = tensor("custom")]; + tensor var_4205_pad_0 = const()[name = tensor("op_4205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(813024320)))]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(813843584)))]; + tensor var_4205_cast_fp16 = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_4203, groups = var_3945, pad = var_4205_pad_0, pad_type = var_4205_pad_type_0, strides = var_4201, weight = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_471_cast_fp16)[name = tensor("op_4205_cast_fp16")]; + tensor hidden_states_293_cast_fp16 = add(x = var_4205_cast_fp16, y = inputs_83_cast_fp16)[name = tensor("hidden_states_293_cast_fp16")]; + tensor var_4207 = const()[name = tensor("op_4207"), val = tensor([2, 320, 48, 80])]; + tensor input_473_cast_fp16 = reshape(shape = var_4207, x = hidden_states_293_cast_fp16)[name = tensor("input_473_cast_fp16")]; + tensor var_4211 = const()[name = tensor("op_4211"), val = tensor([1, 1])]; + tensor var_4213 = const()[name = tensor("op_4213"), val = tensor([1, 1])]; + tensor hidden_states_295_pad_type_0 = const()[name = tensor("hidden_states_295_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_295_pad_0 = const()[name = tensor("hidden_states_295_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(813844288)))]; + tensor up_blocks_3_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(814049152)))]; + tensor hidden_states_295_cast_fp16 = conv(bias = up_blocks_3_attentions_0_proj_out_bias_to_fp16, dilations = var_4213, groups = var_3945, pad = hidden_states_295_pad_0, pad_type = hidden_states_295_pad_type_0, strides = var_4211, weight = up_blocks_3_attentions_0_proj_out_weight_to_fp16, x = input_473_cast_fp16)[name = tensor("hidden_states_295_cast_fp16")]; + tensor hidden_states_297_cast_fp16 = add(x = hidden_states_295_cast_fp16, y = hidden_states_283_cast_fp16)[name = tensor("hidden_states_297_cast_fp16")]; + tensor input_475_interleave_0 = const()[name = tensor("input_475_interleave_0"), val = tensor(false)]; + tensor cast_11 = cast(dtype = cast_5_dtype_0, x = input_35_cast_fp16)[name = tensor("cast_11")]; + tensor input_475_cast_fp16 = concat(axis = var_3945, interleave = input_475_interleave_0, values = (hidden_states_297_cast_fp16, cast_11))[name = tensor("input_475_cast_fp16")]; + tensor reshape_216_shape_0 = const()[name = tensor("reshape_216_shape_0"), val = tensor([2, 32, 20, 48, 80])]; + tensor reshape_216_cast_fp16 = reshape(shape = reshape_216_shape_0, x = input_475_cast_fp16)[name = tensor("reshape_216_cast_fp16")]; + tensor reduce_mean_162_axes_0 = const()[name = tensor("reduce_mean_162_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_162_keep_dims_0 = const()[name = tensor("reduce_mean_162_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_162_cast_fp16 = reduce_mean(axes = reduce_mean_162_axes_0, keep_dims = reduce_mean_162_keep_dims_0, x = reshape_216_cast_fp16)[name = tensor("reduce_mean_162_cast_fp16")]; + tensor sub_108_cast_fp16 = sub(x = reshape_216_cast_fp16, y = reduce_mean_162_cast_fp16)[name = tensor("sub_108_cast_fp16")]; + tensor square_54_cast_fp16 = square(x = sub_108_cast_fp16)[name = tensor("square_54_cast_fp16")]; + tensor reduce_mean_164_axes_0 = const()[name = tensor("reduce_mean_164_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_164_keep_dims_0 = const()[name = tensor("reduce_mean_164_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_164_cast_fp16 = reduce_mean(axes = reduce_mean_164_axes_0, keep_dims = reduce_mean_164_keep_dims_0, x = square_54_cast_fp16)[name = tensor("reduce_mean_164_cast_fp16")]; + tensor add_108_y_0_to_fp16 = const()[name = tensor("add_108_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_108_cast_fp16 = add(x = reduce_mean_164_cast_fp16, y = add_108_y_0_to_fp16)[name = tensor("add_108_cast_fp16")]; + tensor sqrt_54_cast_fp16 = sqrt(x = add_108_cast_fp16)[name = tensor("sqrt_54_cast_fp16")]; + tensor real_div_54_cast_fp16 = real_div(x = sub_108_cast_fp16, y = sqrt_54_cast_fp16)[name = tensor("real_div_54_cast_fp16")]; + tensor reshape_217_shape_0 = const()[name = tensor("reshape_217_shape_0"), val = tensor([2, 640, 48, 80])]; + tensor reshape_217_cast_fp16 = reshape(shape = reshape_217_shape_0, x = real_div_54_cast_fp16)[name = tensor("reshape_217_cast_fp16")]; + tensor add_109_gamma_0_to_fp16 = const()[name = tensor("add_109_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(814049856)))]; + tensor add_109_beta_0_to_fp16 = const()[name = tensor("add_109_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(814051200)))]; + tensor add_109_epsilon_0_to_fp16 = const()[name = tensor("add_109_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_109_cast_fp16 = batch_norm(beta = add_109_beta_0_to_fp16, epsilon = add_109_epsilon_0_to_fp16, gamma = add_109_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_217_cast_fp16)[name = tensor("add_109_cast_fp16")]; + tensor input_479_cast_fp16 = silu(x = add_109_cast_fp16)[name = tensor("input_479_cast_fp16")]; + tensor var_4231 = const()[name = tensor("op_4231"), val = tensor([1, 1])]; + tensor var_4233 = const()[name = tensor("op_4233"), val = tensor([1, 1])]; + tensor hidden_states_299_pad_type_0 = const()[name = tensor("hidden_states_299_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_299_pad_0 = const()[name = tensor("hidden_states_299_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(814052544)))]; + tensor up_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(817739008)))]; + tensor hidden_states_299_cast_fp16 = conv(bias = up_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = var_4233, groups = var_3945, pad = hidden_states_299_pad_0, pad_type = hidden_states_299_pad_type_0, strides = var_4231, weight = up_blocks_3_resnets_1_conv1_weight_to_fp16, x = input_479_cast_fp16)[name = tensor("hidden_states_299_cast_fp16")]; + tensor var_4239 = const()[name = tensor("op_4239"), val = tensor([1, 1])]; + tensor var_4241 = const()[name = tensor("op_4241"), val = tensor([1, 1])]; + tensor temb_41_pad_type_0 = const()[name = tensor("temb_41_pad_type_0"), val = tensor("custom")]; + tensor temb_41_pad_0 = const()[name = tensor("temb_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(817739712)))]; + tensor up_blocks_3_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(818558976)))]; + tensor temb_41_cast_fp16 = conv(bias = up_blocks_3_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_4241, groups = var_3945, pad = temb_41_pad_0, pad_type = temb_41_pad_type_0, strides = var_4239, weight = up_blocks_3_resnets_1_time_emb_proj_weight_to_fp16, x = cast_12)[name = tensor("temb_41_cast_fp16")]; + tensor input_483_cast_fp16 = add(x = hidden_states_299_cast_fp16, y = temb_41_cast_fp16)[name = tensor("input_483_cast_fp16")]; + tensor reshape_220_shape_0 = const()[name = tensor("reshape_220_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_220_cast_fp16 = reshape(shape = reshape_220_shape_0, x = input_483_cast_fp16)[name = tensor("reshape_220_cast_fp16")]; + tensor reduce_mean_165_axes_0 = const()[name = tensor("reduce_mean_165_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_165_keep_dims_0 = const()[name = tensor("reduce_mean_165_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_165_cast_fp16 = reduce_mean(axes = reduce_mean_165_axes_0, keep_dims = reduce_mean_165_keep_dims_0, x = reshape_220_cast_fp16)[name = tensor("reduce_mean_165_cast_fp16")]; + tensor sub_110_cast_fp16 = sub(x = reshape_220_cast_fp16, y = reduce_mean_165_cast_fp16)[name = tensor("sub_110_cast_fp16")]; + tensor square_55_cast_fp16 = square(x = sub_110_cast_fp16)[name = tensor("square_55_cast_fp16")]; + tensor reduce_mean_167_axes_0 = const()[name = tensor("reduce_mean_167_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_167_keep_dims_0 = const()[name = tensor("reduce_mean_167_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_167_cast_fp16 = reduce_mean(axes = reduce_mean_167_axes_0, keep_dims = reduce_mean_167_keep_dims_0, x = square_55_cast_fp16)[name = tensor("reduce_mean_167_cast_fp16")]; + tensor add_110_y_0_to_fp16 = const()[name = tensor("add_110_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_110_cast_fp16 = add(x = reduce_mean_167_cast_fp16, y = add_110_y_0_to_fp16)[name = tensor("add_110_cast_fp16")]; + tensor sqrt_55_cast_fp16 = sqrt(x = add_110_cast_fp16)[name = tensor("sqrt_55_cast_fp16")]; + tensor real_div_55_cast_fp16 = real_div(x = sub_110_cast_fp16, y = sqrt_55_cast_fp16)[name = tensor("real_div_55_cast_fp16")]; + tensor reshape_221_shape_0 = const()[name = tensor("reshape_221_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_221_cast_fp16 = reshape(shape = reshape_221_shape_0, x = real_div_55_cast_fp16)[name = tensor("reshape_221_cast_fp16")]; + tensor add_111_gamma_0_to_fp16 = const()[name = tensor("add_111_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(818559680)))]; + tensor add_111_beta_0_to_fp16 = const()[name = tensor("add_111_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(818560384)))]; + tensor add_111_epsilon_0_to_fp16 = const()[name = tensor("add_111_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_111_cast_fp16 = batch_norm(beta = add_111_beta_0_to_fp16, epsilon = add_111_epsilon_0_to_fp16, gamma = add_111_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_221_cast_fp16)[name = tensor("add_111_cast_fp16")]; + tensor input_487_cast_fp16 = silu(x = add_111_cast_fp16)[name = tensor("input_487_cast_fp16")]; + tensor var_4251 = const()[name = tensor("op_4251"), val = tensor([1, 1])]; + tensor var_4253 = const()[name = tensor("op_4253"), val = tensor([1, 1])]; + tensor hidden_states_301_pad_type_0 = const()[name = tensor("hidden_states_301_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_301_pad_0 = const()[name = tensor("hidden_states_301_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(818561088)))]; + tensor up_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(820404352)))]; + tensor hidden_states_301_cast_fp16 = conv(bias = up_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = var_4253, groups = var_3945, pad = hidden_states_301_pad_0, pad_type = hidden_states_301_pad_type_0, strides = var_4251, weight = up_blocks_3_resnets_1_conv2_weight_to_fp16, x = input_487_cast_fp16)[name = tensor("hidden_states_301_cast_fp16")]; + tensor var_4258 = const()[name = tensor("op_4258"), val = tensor([1, 1])]; + tensor var_4260 = const()[name = tensor("op_4260"), val = tensor([1, 1])]; + tensor x_25_pad_type_0 = const()[name = tensor("x_25_pad_type_0"), val = tensor("custom")]; + tensor x_25_pad_0 = const()[name = tensor("x_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_1_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(820405056)))]; + tensor up_blocks_3_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(820814720)))]; + tensor x_25_cast_fp16 = conv(bias = up_blocks_3_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_4260, groups = var_3945, pad = x_25_pad_0, pad_type = x_25_pad_type_0, strides = var_4258, weight = up_blocks_3_resnets_1_conv_shortcut_weight_to_fp16, x = input_475_cast_fp16)[name = tensor("x_25_cast_fp16")]; + tensor hidden_states_303_cast_fp16 = add(x = x_25_cast_fp16, y = hidden_states_301_cast_fp16)[name = tensor("hidden_states_303_cast_fp16")]; + tensor reshape_224_shape_0 = const()[name = tensor("reshape_224_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_224_cast_fp16 = reshape(shape = reshape_224_shape_0, x = hidden_states_303_cast_fp16)[name = tensor("reshape_224_cast_fp16")]; + tensor reduce_mean_168_axes_0 = const()[name = tensor("reduce_mean_168_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_168_keep_dims_0 = const()[name = tensor("reduce_mean_168_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_168_cast_fp16 = reduce_mean(axes = reduce_mean_168_axes_0, keep_dims = reduce_mean_168_keep_dims_0, x = reshape_224_cast_fp16)[name = tensor("reduce_mean_168_cast_fp16")]; + tensor sub_112_cast_fp16 = sub(x = reshape_224_cast_fp16, y = reduce_mean_168_cast_fp16)[name = tensor("sub_112_cast_fp16")]; + tensor square_56_cast_fp16 = square(x = sub_112_cast_fp16)[name = tensor("square_56_cast_fp16")]; + tensor reduce_mean_170_axes_0 = const()[name = tensor("reduce_mean_170_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_170_keep_dims_0 = const()[name = tensor("reduce_mean_170_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_170_cast_fp16 = reduce_mean(axes = reduce_mean_170_axes_0, keep_dims = reduce_mean_170_keep_dims_0, x = square_56_cast_fp16)[name = tensor("reduce_mean_170_cast_fp16")]; + tensor add_112_y_0_to_fp16 = const()[name = tensor("add_112_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_112_cast_fp16 = add(x = reduce_mean_170_cast_fp16, y = add_112_y_0_to_fp16)[name = tensor("add_112_cast_fp16")]; + tensor sqrt_56_cast_fp16 = sqrt(x = add_112_cast_fp16)[name = tensor("sqrt_56_cast_fp16")]; + tensor real_div_56_cast_fp16 = real_div(x = sub_112_cast_fp16, y = sqrt_56_cast_fp16)[name = tensor("real_div_56_cast_fp16")]; + tensor reshape_225_shape_0 = const()[name = tensor("reshape_225_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_225_cast_fp16 = reshape(shape = reshape_225_shape_0, x = real_div_56_cast_fp16)[name = tensor("reshape_225_cast_fp16")]; + tensor add_113_gamma_0_to_fp16 = const()[name = tensor("add_113_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(820815424)))]; + tensor add_113_beta_0_to_fp16 = const()[name = tensor("add_113_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(820816128)))]; + tensor add_113_epsilon_0_to_fp16 = const()[name = tensor("add_113_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_113_cast_fp16 = batch_norm(beta = add_113_beta_0_to_fp16, epsilon = add_113_epsilon_0_to_fp16, gamma = add_113_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_225_cast_fp16)[name = tensor("add_113_cast_fp16")]; + tensor var_4280 = const()[name = tensor("op_4280"), val = tensor([1, 1])]; + tensor var_4282 = const()[name = tensor("op_4282"), val = tensor([1, 1])]; + tensor hidden_states_305_pad_type_0 = const()[name = tensor("hidden_states_305_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_305_pad_0 = const()[name = tensor("hidden_states_305_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(820816832)))]; + tensor up_blocks_3_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(821021696)))]; + tensor hidden_states_305_cast_fp16 = conv(bias = up_blocks_3_attentions_1_proj_in_bias_to_fp16, dilations = var_4282, groups = var_3945, pad = hidden_states_305_pad_0, pad_type = hidden_states_305_pad_type_0, strides = var_4280, weight = up_blocks_3_attentions_1_proj_in_weight_to_fp16, x = add_113_cast_fp16)[name = tensor("hidden_states_305_cast_fp16")]; + tensor var_4287 = const()[name = tensor("op_4287"), val = tensor([2, 320, 1, 3840])]; + tensor inputs_85_cast_fp16 = reshape(shape = var_4287, x = hidden_states_305_cast_fp16)[name = tensor("inputs_85_cast_fp16")]; + tensor var_4297 = const()[name = tensor("op_4297"), val = tensor([1])]; + tensor channels_mean_85_cast_fp16 = reduce_mean(axes = var_4297, keep_dims = var_3940, x = inputs_85_cast_fp16)[name = tensor("channels_mean_85_cast_fp16")]; + tensor zero_mean_85_cast_fp16 = sub(x = inputs_85_cast_fp16, y = channels_mean_85_cast_fp16)[name = tensor("zero_mean_85_cast_fp16")]; + tensor zero_mean_sq_85_cast_fp16 = mul(x = zero_mean_85_cast_fp16, y = zero_mean_85_cast_fp16)[name = tensor("zero_mean_sq_85_cast_fp16")]; + tensor var_4301 = const()[name = tensor("op_4301"), val = tensor([1])]; + tensor var_4302_cast_fp16 = reduce_mean(axes = var_4301, keep_dims = var_3940, x = zero_mean_sq_85_cast_fp16)[name = tensor("op_4302_cast_fp16")]; + tensor var_4303_to_fp16 = const()[name = tensor("op_4303_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4304_cast_fp16 = add(x = var_4302_cast_fp16, y = var_4303_to_fp16)[name = tensor("op_4304_cast_fp16")]; + tensor denom_85_epsilon_0_to_fp16 = const()[name = tensor("denom_85_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_85_cast_fp16 = rsqrt(epsilon = denom_85_epsilon_0_to_fp16, x = var_4304_cast_fp16)[name = tensor("denom_85_cast_fp16")]; + tensor out_85_cast_fp16 = mul(x = zero_mean_85_cast_fp16, y = denom_85_cast_fp16)[name = tensor("out_85_cast_fp16")]; + tensor var_4308_to_fp16 = const()[name = tensor("op_4308_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(821022400)))]; + tensor var_4309_cast_fp16 = add(x = out_85_cast_fp16, y = var_4308_to_fp16)[name = tensor("op_4309_cast_fp16")]; + tensor var_4311_to_fp16 = const()[name = tensor("op_4311_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(821023104)))]; + tensor hidden_states_307_cast_fp16 = mul(x = var_4309_cast_fp16, y = var_4311_to_fp16)[name = tensor("hidden_states_307_cast_fp16")]; + tensor var_4318 = const()[name = tensor("op_4318"), val = tensor([1, 1])]; + tensor var_4320 = const()[name = tensor("op_4320"), val = tensor([1, 1])]; + tensor q_57_pad_type_0 = const()[name = tensor("q_57_pad_type_0"), val = tensor("custom")]; + tensor q_57_pad_0 = const()[name = tensor("q_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(821023808)))]; + tensor q_57_cast_fp16 = conv(dilations = var_4320, groups = var_3945, pad = q_57_pad_0, pad_type = q_57_pad_type_0, strides = var_4318, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_307_cast_fp16)[name = tensor("q_57_cast_fp16")]; + tensor var_4324 = const()[name = tensor("op_4324"), val = tensor([1, 1])]; + tensor var_4326 = const()[name = tensor("op_4326"), val = tensor([1, 1])]; + tensor k_57_pad_type_0 = const()[name = tensor("k_57_pad_type_0"), val = tensor("custom")]; + tensor k_57_pad_0 = const()[name = tensor("k_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(821228672)))]; + tensor k_57_cast_fp16 = conv(dilations = var_4326, groups = var_3945, pad = k_57_pad_0, pad_type = k_57_pad_type_0, strides = var_4324, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_307_cast_fp16)[name = tensor("k_57_cast_fp16")]; + tensor var_4330 = const()[name = tensor("op_4330"), val = tensor([1, 1])]; + tensor var_4332 = const()[name = tensor("op_4332"), val = tensor([1, 1])]; + tensor v_57_pad_type_0 = const()[name = tensor("v_57_pad_type_0"), val = tensor("custom")]; + tensor v_57_pad_0 = const()[name = tensor("v_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(821433536)))]; + tensor v_57_cast_fp16 = conv(dilations = var_4332, groups = var_3945, pad = v_57_pad_0, pad_type = v_57_pad_type_0, strides = var_4330, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_307_cast_fp16)[name = tensor("v_57_cast_fp16")]; + tensor var_4336 = const()[name = tensor("op_4336"), val = tensor([2, 5, 64, -1])]; + tensor var_4337_cast_fp16 = reshape(shape = var_4336, x = q_57_cast_fp16)[name = tensor("op_4337_cast_fp16")]; + tensor var_4338 = const()[name = tensor("op_4338"), val = tensor([2, 5, 64, -1])]; + tensor var_4339_cast_fp16 = reshape(shape = var_4338, x = k_57_cast_fp16)[name = tensor("op_4339_cast_fp16")]; + tensor var_4340 = const()[name = tensor("op_4340"), val = tensor([2, 5, 64, -1])]; + tensor var_4341_cast_fp16 = reshape(shape = var_4340, x = v_57_cast_fp16)[name = tensor("op_4341_cast_fp16")]; + tensor attn_weights_113_transpose_x_0 = const()[name = tensor("attn_weights_113_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_113_transpose_y_0 = const()[name = tensor("attn_weights_113_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_113_cast_fp16 = matmul(transpose_x = attn_weights_113_transpose_x_0, transpose_y = attn_weights_113_transpose_y_0, x = var_4337_cast_fp16, y = var_4339_cast_fp16)[name = tensor("attn_weights_113_cast_fp16")]; + tensor attn_weights_115_cast_fp16 = mul(x = attn_weights_113_cast_fp16, y = var_3936_to_fp16)[name = tensor("attn_weights_115_cast_fp16")]; + tensor var_4345_cast_fp16 = softmax(axis = var_3929, x = attn_weights_115_cast_fp16)[name = tensor("op_4345_cast_fp16")]; + tensor attn_57_transpose_x_0 = const()[name = tensor("attn_57_transpose_x_0"), val = tensor(false)]; + tensor attn_57_transpose_y_0 = const()[name = tensor("attn_57_transpose_y_0"), val = tensor(true)]; + tensor attn_57_cast_fp16 = matmul(transpose_x = attn_57_transpose_x_0, transpose_y = attn_57_transpose_y_0, x = var_4341_cast_fp16, y = var_4345_cast_fp16)[name = tensor("attn_57_cast_fp16")]; + tensor var_4349 = const()[name = tensor("op_4349"), val = tensor([2, 320, 1, -1])]; + tensor input_491_cast_fp16 = reshape(shape = var_4349, x = attn_57_cast_fp16)[name = tensor("input_491_cast_fp16")]; + tensor var_4354 = const()[name = tensor("op_4354"), val = tensor([1, 1])]; + tensor var_4356 = const()[name = tensor("op_4356"), val = tensor([1, 1])]; + tensor var_4358_pad_type_0 = const()[name = tensor("op_4358_pad_type_0"), val = tensor("custom")]; + tensor var_4358_pad_0 = const()[name = tensor("op_4358_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(821638400)))]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(821843264)))]; + tensor var_4358_cast_fp16 = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_4356, groups = var_3945, pad = var_4358_pad_0, pad_type = var_4358_pad_type_0, strides = var_4354, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_491_cast_fp16)[name = tensor("op_4358_cast_fp16")]; + tensor inputs_87_cast_fp16 = add(x = var_4358_cast_fp16, y = inputs_85_cast_fp16)[name = tensor("inputs_87_cast_fp16")]; + tensor var_4362 = const()[name = tensor("op_4362"), val = tensor([1])]; + tensor channels_mean_87_cast_fp16 = reduce_mean(axes = var_4362, keep_dims = var_3940, x = inputs_87_cast_fp16)[name = tensor("channels_mean_87_cast_fp16")]; + tensor zero_mean_87_cast_fp16 = sub(x = inputs_87_cast_fp16, y = channels_mean_87_cast_fp16)[name = tensor("zero_mean_87_cast_fp16")]; + tensor zero_mean_sq_87_cast_fp16 = mul(x = zero_mean_87_cast_fp16, y = zero_mean_87_cast_fp16)[name = tensor("zero_mean_sq_87_cast_fp16")]; + tensor var_4366 = const()[name = tensor("op_4366"), val = tensor([1])]; + tensor var_4367_cast_fp16 = reduce_mean(axes = var_4366, keep_dims = var_3940, x = zero_mean_sq_87_cast_fp16)[name = tensor("op_4367_cast_fp16")]; + tensor var_4368_to_fp16 = const()[name = tensor("op_4368_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4369_cast_fp16 = add(x = var_4367_cast_fp16, y = var_4368_to_fp16)[name = tensor("op_4369_cast_fp16")]; + tensor denom_87_epsilon_0_to_fp16 = const()[name = tensor("denom_87_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_87_cast_fp16 = rsqrt(epsilon = denom_87_epsilon_0_to_fp16, x = var_4369_cast_fp16)[name = tensor("denom_87_cast_fp16")]; + tensor out_87_cast_fp16 = mul(x = zero_mean_87_cast_fp16, y = denom_87_cast_fp16)[name = tensor("out_87_cast_fp16")]; + tensor var_4373_to_fp16 = const()[name = tensor("op_4373_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(821843968)))]; + tensor var_4374_cast_fp16 = add(x = out_87_cast_fp16, y = var_4373_to_fp16)[name = tensor("op_4374_cast_fp16")]; + tensor var_4376_to_fp16 = const()[name = tensor("op_4376_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(821844672)))]; + tensor hidden_states_309_cast_fp16 = mul(x = var_4374_cast_fp16, y = var_4376_to_fp16)[name = tensor("hidden_states_309_cast_fp16")]; + tensor var_4383 = const()[name = tensor("op_4383"), val = tensor([1, 1])]; + tensor var_4385 = const()[name = tensor("op_4385"), val = tensor([1, 1])]; + tensor q_59_pad_type_0 = const()[name = tensor("q_59_pad_type_0"), val = tensor("custom")]; + tensor q_59_pad_0 = const()[name = tensor("q_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(821845376)))]; + tensor q_59_cast_fp16 = conv(dilations = var_4385, groups = var_3945, pad = q_59_pad_0, pad_type = q_59_pad_type_0, strides = var_4383, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_309_cast_fp16)[name = tensor("q_59_cast_fp16")]; + tensor var_4389 = const()[name = tensor("op_4389"), val = tensor([1, 1])]; + tensor var_4391 = const()[name = tensor("op_4391"), val = tensor([1, 1])]; + tensor k_59_pad_type_0 = const()[name = tensor("k_59_pad_type_0"), val = tensor("custom")]; + tensor k_59_pad_0 = const()[name = tensor("k_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(822050240)))]; + tensor k_59_cast_fp16 = conv(dilations = var_4391, groups = var_3945, pad = k_59_pad_0, pad_type = k_59_pad_type_0, strides = var_4389, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_59_cast_fp16")]; + tensor var_4395 = const()[name = tensor("op_4395"), val = tensor([1, 1])]; + tensor var_4397 = const()[name = tensor("op_4397"), val = tensor([1, 1])]; + tensor v_59_pad_type_0 = const()[name = tensor("v_59_pad_type_0"), val = tensor("custom")]; + tensor v_59_pad_0 = const()[name = tensor("v_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(822705664)))]; + tensor v_59_cast_fp16 = conv(dilations = var_4397, groups = var_3945, pad = v_59_pad_0, pad_type = v_59_pad_type_0, strides = var_4395, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_59_cast_fp16")]; + tensor var_4401 = const()[name = tensor("op_4401"), val = tensor([2, 5, 64, -1])]; + tensor var_4402_cast_fp16 = reshape(shape = var_4401, x = q_59_cast_fp16)[name = tensor("op_4402_cast_fp16")]; + tensor var_4403 = const()[name = tensor("op_4403"), val = tensor([2, 5, 64, -1])]; + tensor var_4404_cast_fp16 = reshape(shape = var_4403, x = k_59_cast_fp16)[name = tensor("op_4404_cast_fp16")]; + tensor var_4405 = const()[name = tensor("op_4405"), val = tensor([2, 5, 64, -1])]; + tensor var_4406_cast_fp16 = reshape(shape = var_4405, x = v_59_cast_fp16)[name = tensor("op_4406_cast_fp16")]; + tensor attn_weights_117_transpose_x_0 = const()[name = tensor("attn_weights_117_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_117_transpose_y_0 = const()[name = tensor("attn_weights_117_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_117_cast_fp16 = matmul(transpose_x = attn_weights_117_transpose_x_0, transpose_y = attn_weights_117_transpose_y_0, x = var_4402_cast_fp16, y = var_4404_cast_fp16)[name = tensor("attn_weights_117_cast_fp16")]; + tensor attn_weights_119_cast_fp16 = mul(x = attn_weights_117_cast_fp16, y = var_3936_to_fp16)[name = tensor("attn_weights_119_cast_fp16")]; + tensor var_4410_cast_fp16 = softmax(axis = var_3929, x = attn_weights_119_cast_fp16)[name = tensor("op_4410_cast_fp16")]; + tensor attn_59_transpose_x_0 = const()[name = tensor("attn_59_transpose_x_0"), val = tensor(false)]; + tensor attn_59_transpose_y_0 = const()[name = tensor("attn_59_transpose_y_0"), val = tensor(true)]; + tensor attn_59_cast_fp16 = matmul(transpose_x = attn_59_transpose_x_0, transpose_y = attn_59_transpose_y_0, x = var_4406_cast_fp16, y = var_4410_cast_fp16)[name = tensor("attn_59_cast_fp16")]; + tensor var_4414 = const()[name = tensor("op_4414"), val = tensor([2, 320, 1, -1])]; + tensor input_493_cast_fp16 = reshape(shape = var_4414, x = attn_59_cast_fp16)[name = tensor("input_493_cast_fp16")]; + tensor var_4419 = const()[name = tensor("op_4419"), val = tensor([1, 1])]; + tensor var_4421 = const()[name = tensor("op_4421"), val = tensor([1, 1])]; + tensor var_4423_pad_type_0 = const()[name = tensor("op_4423_pad_type_0"), val = tensor("custom")]; + tensor var_4423_pad_0 = const()[name = tensor("op_4423_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(823361088)))]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(823565952)))]; + tensor var_4423_cast_fp16 = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_4421, groups = var_3945, pad = var_4423_pad_0, pad_type = var_4423_pad_type_0, strides = var_4419, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_493_cast_fp16)[name = tensor("op_4423_cast_fp16")]; + tensor inputs_89_cast_fp16 = add(x = var_4423_cast_fp16, y = inputs_87_cast_fp16)[name = tensor("inputs_89_cast_fp16")]; + tensor var_4427 = const()[name = tensor("op_4427"), val = tensor([1])]; + tensor channels_mean_89_cast_fp16 = reduce_mean(axes = var_4427, keep_dims = var_3940, x = inputs_89_cast_fp16)[name = tensor("channels_mean_89_cast_fp16")]; + tensor zero_mean_89_cast_fp16 = sub(x = inputs_89_cast_fp16, y = channels_mean_89_cast_fp16)[name = tensor("zero_mean_89_cast_fp16")]; + tensor zero_mean_sq_89_cast_fp16 = mul(x = zero_mean_89_cast_fp16, y = zero_mean_89_cast_fp16)[name = tensor("zero_mean_sq_89_cast_fp16")]; + tensor var_4431 = const()[name = tensor("op_4431"), val = tensor([1])]; + tensor var_4432_cast_fp16 = reduce_mean(axes = var_4431, keep_dims = var_3940, x = zero_mean_sq_89_cast_fp16)[name = tensor("op_4432_cast_fp16")]; + tensor var_4433_to_fp16 = const()[name = tensor("op_4433_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4434_cast_fp16 = add(x = var_4432_cast_fp16, y = var_4433_to_fp16)[name = tensor("op_4434_cast_fp16")]; + tensor denom_89_epsilon_0_to_fp16 = const()[name = tensor("denom_89_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_89_cast_fp16 = rsqrt(epsilon = denom_89_epsilon_0_to_fp16, x = var_4434_cast_fp16)[name = tensor("denom_89_cast_fp16")]; + tensor out_89_cast_fp16 = mul(x = zero_mean_89_cast_fp16, y = denom_89_cast_fp16)[name = tensor("out_89_cast_fp16")]; + tensor var_4438_to_fp16 = const()[name = tensor("op_4438_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(823566656)))]; + tensor var_4439_cast_fp16 = add(x = out_89_cast_fp16, y = var_4438_to_fp16)[name = tensor("op_4439_cast_fp16")]; + tensor var_4441_to_fp16 = const()[name = tensor("op_4441_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(823567360)))]; + tensor input_495_cast_fp16 = mul(x = var_4439_cast_fp16, y = var_4441_to_fp16)[name = tensor("input_495_cast_fp16")]; + tensor var_4449 = const()[name = tensor("op_4449"), val = tensor([1, 1])]; + tensor var_4451 = const()[name = tensor("op_4451"), val = tensor([1, 1])]; + tensor var_4453_pad_type_0 = const()[name = tensor("op_4453_pad_type_0"), val = tensor("custom")]; + tensor var_4453_pad_0 = const()[name = tensor("op_4453_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(823568064)))]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(825206528)))]; + tensor var_4453_cast_fp16 = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_4451, groups = var_3945, pad = var_4453_pad_0, pad_type = var_4453_pad_type_0, strides = var_4449, weight = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_495_cast_fp16)[name = tensor("op_4453_cast_fp16")]; + tensor var_4454_split_sizes_0 = const()[name = tensor("op_4454_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_4454_axis_0 = const()[name = tensor("op_4454_axis_0"), val = tensor(1)]; + tensor var_4454_cast_fp16_0, tensor var_4454_cast_fp16_1 = split(axis = var_4454_axis_0, split_sizes = var_4454_split_sizes_0, x = var_4453_cast_fp16)[name = tensor("op_4454_cast_fp16")]; + tensor var_4456_mode_0 = const()[name = tensor("op_4456_mode_0"), val = tensor("EXACT")]; + tensor var_4456_cast_fp16 = gelu(mode = var_4456_mode_0, x = var_4454_cast_fp16_1)[name = tensor("op_4456_cast_fp16")]; + tensor input_497_cast_fp16 = mul(x = var_4454_cast_fp16_0, y = var_4456_cast_fp16)[name = tensor("input_497_cast_fp16")]; + tensor var_4460 = const()[name = tensor("op_4460"), val = tensor([1, 1])]; + tensor var_4462 = const()[name = tensor("op_4462"), val = tensor([1, 1])]; + tensor var_4464_pad_type_0 = const()[name = tensor("op_4464_pad_type_0"), val = tensor("custom")]; + tensor var_4464_pad_0 = const()[name = tensor("op_4464_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(825211712)))]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(826030976)))]; + tensor var_4464_cast_fp16 = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_4462, groups = var_3945, pad = var_4464_pad_0, pad_type = var_4464_pad_type_0, strides = var_4460, weight = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_497_cast_fp16)[name = tensor("op_4464_cast_fp16")]; + tensor hidden_states_313_cast_fp16 = add(x = var_4464_cast_fp16, y = inputs_89_cast_fp16)[name = tensor("hidden_states_313_cast_fp16")]; + tensor var_4466 = const()[name = tensor("op_4466"), val = tensor([2, 320, 48, 80])]; + tensor input_499_cast_fp16 = reshape(shape = var_4466, x = hidden_states_313_cast_fp16)[name = tensor("input_499_cast_fp16")]; + tensor var_4470 = const()[name = tensor("op_4470"), val = tensor([1, 1])]; + tensor var_4472 = const()[name = tensor("op_4472"), val = tensor([1, 1])]; + tensor hidden_states_315_pad_type_0 = const()[name = tensor("hidden_states_315_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_315_pad_0 = const()[name = tensor("hidden_states_315_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(826031680)))]; + tensor up_blocks_3_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(826236544)))]; + tensor hidden_states_315_cast_fp16 = conv(bias = up_blocks_3_attentions_1_proj_out_bias_to_fp16, dilations = var_4472, groups = var_3945, pad = hidden_states_315_pad_0, pad_type = hidden_states_315_pad_type_0, strides = var_4470, weight = up_blocks_3_attentions_1_proj_out_weight_to_fp16, x = input_499_cast_fp16)[name = tensor("hidden_states_315_cast_fp16")]; + tensor hidden_states_317_cast_fp16 = add(x = hidden_states_315_cast_fp16, y = hidden_states_303_cast_fp16)[name = tensor("hidden_states_317_cast_fp16")]; + tensor input_501_interleave_0 = const()[name = tensor("input_501_interleave_0"), val = tensor(false)]; + tensor cast_13 = cast(dtype = cast_0_dtype_0, x = input_7_cast_fp16)[name = tensor("cast_13")]; + tensor input_501_cast_fp16 = concat(axis = var_3945, interleave = input_501_interleave_0, values = (hidden_states_317_cast_fp16, cast_13))[name = tensor("input_501_cast_fp16")]; + tensor reshape_228_shape_0 = const()[name = tensor("reshape_228_shape_0"), val = tensor([2, 32, 20, 48, 80])]; + tensor reshape_228_cast_fp16 = reshape(shape = reshape_228_shape_0, x = input_501_cast_fp16)[name = tensor("reshape_228_cast_fp16")]; + tensor reduce_mean_171_axes_0 = const()[name = tensor("reduce_mean_171_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_171_keep_dims_0 = const()[name = tensor("reduce_mean_171_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_171_cast_fp16 = reduce_mean(axes = reduce_mean_171_axes_0, keep_dims = reduce_mean_171_keep_dims_0, x = reshape_228_cast_fp16)[name = tensor("reduce_mean_171_cast_fp16")]; + tensor sub_114_cast_fp16 = sub(x = reshape_228_cast_fp16, y = reduce_mean_171_cast_fp16)[name = tensor("sub_114_cast_fp16")]; + tensor square_57_cast_fp16 = square(x = sub_114_cast_fp16)[name = tensor("square_57_cast_fp16")]; + tensor reduce_mean_173_axes_0 = const()[name = tensor("reduce_mean_173_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_173_keep_dims_0 = const()[name = tensor("reduce_mean_173_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_173_cast_fp16 = reduce_mean(axes = reduce_mean_173_axes_0, keep_dims = reduce_mean_173_keep_dims_0, x = square_57_cast_fp16)[name = tensor("reduce_mean_173_cast_fp16")]; + tensor add_114_y_0_to_fp16 = const()[name = tensor("add_114_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_114_cast_fp16 = add(x = reduce_mean_173_cast_fp16, y = add_114_y_0_to_fp16)[name = tensor("add_114_cast_fp16")]; + tensor sqrt_57_cast_fp16 = sqrt(x = add_114_cast_fp16)[name = tensor("sqrt_57_cast_fp16")]; + tensor real_div_57_cast_fp16 = real_div(x = sub_114_cast_fp16, y = sqrt_57_cast_fp16)[name = tensor("real_div_57_cast_fp16")]; + tensor reshape_229_shape_0 = const()[name = tensor("reshape_229_shape_0"), val = tensor([2, 640, 48, 80])]; + tensor reshape_229_cast_fp16 = reshape(shape = reshape_229_shape_0, x = real_div_57_cast_fp16)[name = tensor("reshape_229_cast_fp16")]; + tensor add_115_gamma_0_to_fp16 = const()[name = tensor("add_115_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(826237248)))]; + tensor add_115_beta_0_to_fp16 = const()[name = tensor("add_115_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(826238592)))]; + tensor add_115_epsilon_0_to_fp16 = const()[name = tensor("add_115_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_115_cast_fp16 = batch_norm(beta = add_115_beta_0_to_fp16, epsilon = add_115_epsilon_0_to_fp16, gamma = add_115_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_229_cast_fp16)[name = tensor("add_115_cast_fp16")]; + tensor input_505_cast_fp16 = silu(x = add_115_cast_fp16)[name = tensor("input_505_cast_fp16")]; + tensor var_4490 = const()[name = tensor("op_4490"), val = tensor([1, 1])]; + tensor var_4492 = const()[name = tensor("op_4492"), val = tensor([1, 1])]; + tensor hidden_states_319_pad_type_0 = const()[name = tensor("hidden_states_319_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_319_pad_0 = const()[name = tensor("hidden_states_319_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(826239936)))]; + tensor up_blocks_3_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(829926400)))]; + tensor hidden_states_319_cast_fp16 = conv(bias = up_blocks_3_resnets_2_conv1_bias_to_fp16, dilations = var_4492, groups = var_3945, pad = hidden_states_319_pad_0, pad_type = hidden_states_319_pad_type_0, strides = var_4490, weight = up_blocks_3_resnets_2_conv1_weight_to_fp16, x = input_505_cast_fp16)[name = tensor("hidden_states_319_cast_fp16")]; + tensor var_4498 = const()[name = tensor("op_4498"), val = tensor([1, 1])]; + tensor var_4500 = const()[name = tensor("op_4500"), val = tensor([1, 1])]; + tensor temb_pad_type_0 = const()[name = tensor("temb_pad_type_0"), val = tensor("custom")]; + tensor temb_pad_0 = const()[name = tensor("temb_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_2_time_emb_proj_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(829927104)))]; + tensor up_blocks_3_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(830746368)))]; + tensor temb_cast_fp16 = conv(bias = up_blocks_3_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_4500, groups = var_3945, pad = temb_pad_0, pad_type = temb_pad_type_0, strides = var_4498, weight = up_blocks_3_resnets_2_time_emb_proj_weight_to_fp16, x = cast_12)[name = tensor("temb_cast_fp16")]; + tensor input_509_cast_fp16 = add(x = hidden_states_319_cast_fp16, y = temb_cast_fp16)[name = tensor("input_509_cast_fp16")]; + tensor reshape_232_shape_0 = const()[name = tensor("reshape_232_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_232_cast_fp16 = reshape(shape = reshape_232_shape_0, x = input_509_cast_fp16)[name = tensor("reshape_232_cast_fp16")]; + tensor reduce_mean_174_axes_0 = const()[name = tensor("reduce_mean_174_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_174_keep_dims_0 = const()[name = tensor("reduce_mean_174_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_174_cast_fp16 = reduce_mean(axes = reduce_mean_174_axes_0, keep_dims = reduce_mean_174_keep_dims_0, x = reshape_232_cast_fp16)[name = tensor("reduce_mean_174_cast_fp16")]; + tensor sub_116_cast_fp16 = sub(x = reshape_232_cast_fp16, y = reduce_mean_174_cast_fp16)[name = tensor("sub_116_cast_fp16")]; + tensor square_58_cast_fp16 = square(x = sub_116_cast_fp16)[name = tensor("square_58_cast_fp16")]; + tensor reduce_mean_176_axes_0 = const()[name = tensor("reduce_mean_176_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_176_keep_dims_0 = const()[name = tensor("reduce_mean_176_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_176_cast_fp16 = reduce_mean(axes = reduce_mean_176_axes_0, keep_dims = reduce_mean_176_keep_dims_0, x = square_58_cast_fp16)[name = tensor("reduce_mean_176_cast_fp16")]; + tensor add_116_y_0_to_fp16 = const()[name = tensor("add_116_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_116_cast_fp16 = add(x = reduce_mean_176_cast_fp16, y = add_116_y_0_to_fp16)[name = tensor("add_116_cast_fp16")]; + tensor sqrt_58_cast_fp16 = sqrt(x = add_116_cast_fp16)[name = tensor("sqrt_58_cast_fp16")]; + tensor real_div_58_cast_fp16 = real_div(x = sub_116_cast_fp16, y = sqrt_58_cast_fp16)[name = tensor("real_div_58_cast_fp16")]; + tensor reshape_233_shape_0 = const()[name = tensor("reshape_233_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_233_cast_fp16 = reshape(shape = reshape_233_shape_0, x = real_div_58_cast_fp16)[name = tensor("reshape_233_cast_fp16")]; + tensor add_117_gamma_0_to_fp16 = const()[name = tensor("add_117_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(830747072)))]; + tensor add_117_beta_0_to_fp16 = const()[name = tensor("add_117_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(830747776)))]; + tensor add_117_epsilon_0_to_fp16 = const()[name = tensor("add_117_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_117_cast_fp16 = batch_norm(beta = add_117_beta_0_to_fp16, epsilon = add_117_epsilon_0_to_fp16, gamma = add_117_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_233_cast_fp16)[name = tensor("add_117_cast_fp16")]; + tensor input_513_cast_fp16 = silu(x = add_117_cast_fp16)[name = tensor("input_513_cast_fp16")]; + tensor var_4510 = const()[name = tensor("op_4510"), val = tensor([1, 1])]; + tensor var_4512 = const()[name = tensor("op_4512"), val = tensor([1, 1])]; + tensor hidden_states_321_pad_type_0 = const()[name = tensor("hidden_states_321_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_321_pad_0 = const()[name = tensor("hidden_states_321_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(830748480)))]; + tensor up_blocks_3_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(832591744)))]; + tensor hidden_states_321_cast_fp16 = conv(bias = up_blocks_3_resnets_2_conv2_bias_to_fp16, dilations = var_4512, groups = var_3945, pad = hidden_states_321_pad_0, pad_type = hidden_states_321_pad_type_0, strides = var_4510, weight = up_blocks_3_resnets_2_conv2_weight_to_fp16, x = input_513_cast_fp16)[name = tensor("hidden_states_321_cast_fp16")]; + tensor var_4517 = const()[name = tensor("op_4517"), val = tensor([1, 1])]; + tensor var_4519 = const()[name = tensor("op_4519"), val = tensor([1, 1])]; + tensor x_pad_type_0 = const()[name = tensor("x_pad_type_0"), val = tensor("custom")]; + tensor x_pad_0 = const()[name = tensor("x_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_2_conv_shortcut_weight_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(832592448)))]; + tensor up_blocks_3_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833002112)))]; + tensor x_cast_fp16 = conv(bias = up_blocks_3_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_4519, groups = var_3945, pad = x_pad_0, pad_type = x_pad_type_0, strides = var_4517, weight = up_blocks_3_resnets_2_conv_shortcut_weight_to_fp16, x = input_501_cast_fp16)[name = tensor("x_cast_fp16")]; + tensor hidden_states_323_cast_fp16 = add(x = x_cast_fp16, y = hidden_states_321_cast_fp16)[name = tensor("hidden_states_323_cast_fp16")]; + tensor reshape_236_shape_0 = const()[name = tensor("reshape_236_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_236_cast_fp16 = reshape(shape = reshape_236_shape_0, x = hidden_states_323_cast_fp16)[name = tensor("reshape_236_cast_fp16")]; + tensor reduce_mean_177_axes_0 = const()[name = tensor("reduce_mean_177_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_177_keep_dims_0 = const()[name = tensor("reduce_mean_177_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_177_cast_fp16 = reduce_mean(axes = reduce_mean_177_axes_0, keep_dims = reduce_mean_177_keep_dims_0, x = reshape_236_cast_fp16)[name = tensor("reduce_mean_177_cast_fp16")]; + tensor sub_118_cast_fp16 = sub(x = reshape_236_cast_fp16, y = reduce_mean_177_cast_fp16)[name = tensor("sub_118_cast_fp16")]; + tensor square_59_cast_fp16 = square(x = sub_118_cast_fp16)[name = tensor("square_59_cast_fp16")]; + tensor reduce_mean_179_axes_0 = const()[name = tensor("reduce_mean_179_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_179_keep_dims_0 = const()[name = tensor("reduce_mean_179_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_179_cast_fp16 = reduce_mean(axes = reduce_mean_179_axes_0, keep_dims = reduce_mean_179_keep_dims_0, x = square_59_cast_fp16)[name = tensor("reduce_mean_179_cast_fp16")]; + tensor add_118_y_0_to_fp16 = const()[name = tensor("add_118_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_118_cast_fp16 = add(x = reduce_mean_179_cast_fp16, y = add_118_y_0_to_fp16)[name = tensor("add_118_cast_fp16")]; + tensor sqrt_59_cast_fp16 = sqrt(x = add_118_cast_fp16)[name = tensor("sqrt_59_cast_fp16")]; + tensor real_div_59_cast_fp16 = real_div(x = sub_118_cast_fp16, y = sqrt_59_cast_fp16)[name = tensor("real_div_59_cast_fp16")]; + tensor reshape_237_shape_0 = const()[name = tensor("reshape_237_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_237_cast_fp16 = reshape(shape = reshape_237_shape_0, x = real_div_59_cast_fp16)[name = tensor("reshape_237_cast_fp16")]; + tensor add_119_gamma_0_to_fp16 = const()[name = tensor("add_119_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833002816)))]; + tensor add_119_beta_0_to_fp16 = const()[name = tensor("add_119_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833003520)))]; + tensor add_119_epsilon_0_to_fp16 = const()[name = tensor("add_119_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_119_cast_fp16 = batch_norm(beta = add_119_beta_0_to_fp16, epsilon = add_119_epsilon_0_to_fp16, gamma = add_119_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_237_cast_fp16)[name = tensor("add_119_cast_fp16")]; + tensor var_4539 = const()[name = tensor("op_4539"), val = tensor([1, 1])]; + tensor var_4541 = const()[name = tensor("op_4541"), val = tensor([1, 1])]; + tensor hidden_states_325_pad_type_0 = const()[name = tensor("hidden_states_325_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_325_pad_0 = const()[name = tensor("hidden_states_325_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_proj_in_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833004224)))]; + tensor up_blocks_3_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833209088)))]; + tensor hidden_states_325_cast_fp16 = conv(bias = up_blocks_3_attentions_2_proj_in_bias_to_fp16, dilations = var_4541, groups = var_3945, pad = hidden_states_325_pad_0, pad_type = hidden_states_325_pad_type_0, strides = var_4539, weight = up_blocks_3_attentions_2_proj_in_weight_to_fp16, x = add_119_cast_fp16)[name = tensor("hidden_states_325_cast_fp16")]; + tensor var_4546 = const()[name = tensor("op_4546"), val = tensor([2, 320, 1, 3840])]; + tensor inputs_91_cast_fp16 = reshape(shape = var_4546, x = hidden_states_325_cast_fp16)[name = tensor("inputs_91_cast_fp16")]; + tensor var_4556 = const()[name = tensor("op_4556"), val = tensor([1])]; + tensor channels_mean_91_cast_fp16 = reduce_mean(axes = var_4556, keep_dims = var_3940, x = inputs_91_cast_fp16)[name = tensor("channels_mean_91_cast_fp16")]; + tensor zero_mean_91_cast_fp16 = sub(x = inputs_91_cast_fp16, y = channels_mean_91_cast_fp16)[name = tensor("zero_mean_91_cast_fp16")]; + tensor zero_mean_sq_91_cast_fp16 = mul(x = zero_mean_91_cast_fp16, y = zero_mean_91_cast_fp16)[name = tensor("zero_mean_sq_91_cast_fp16")]; + tensor var_4560 = const()[name = tensor("op_4560"), val = tensor([1])]; + tensor var_4561_cast_fp16 = reduce_mean(axes = var_4560, keep_dims = var_3940, x = zero_mean_sq_91_cast_fp16)[name = tensor("op_4561_cast_fp16")]; + tensor var_4562_to_fp16 = const()[name = tensor("op_4562_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4563_cast_fp16 = add(x = var_4561_cast_fp16, y = var_4562_to_fp16)[name = tensor("op_4563_cast_fp16")]; + tensor denom_91_epsilon_0_to_fp16 = const()[name = tensor("denom_91_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_91_cast_fp16 = rsqrt(epsilon = denom_91_epsilon_0_to_fp16, x = var_4563_cast_fp16)[name = tensor("denom_91_cast_fp16")]; + tensor out_91_cast_fp16 = mul(x = zero_mean_91_cast_fp16, y = denom_91_cast_fp16)[name = tensor("out_91_cast_fp16")]; + tensor var_4567_to_fp16 = const()[name = tensor("op_4567_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833209792)))]; + tensor var_4568_cast_fp16 = add(x = out_91_cast_fp16, y = var_4567_to_fp16)[name = tensor("op_4568_cast_fp16")]; + tensor var_4570_to_fp16 = const()[name = tensor("op_4570_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833210496)))]; + tensor hidden_states_327_cast_fp16 = mul(x = var_4568_cast_fp16, y = var_4570_to_fp16)[name = tensor("hidden_states_327_cast_fp16")]; + tensor var_4577 = const()[name = tensor("op_4577"), val = tensor([1, 1])]; + tensor var_4579 = const()[name = tensor("op_4579"), val = tensor([1, 1])]; + tensor q_61_pad_type_0 = const()[name = tensor("q_61_pad_type_0"), val = tensor("custom")]; + tensor q_61_pad_0 = const()[name = tensor("q_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833211200)))]; + tensor q_61_cast_fp16 = conv(dilations = var_4579, groups = var_3945, pad = q_61_pad_0, pad_type = q_61_pad_type_0, strides = var_4577, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_327_cast_fp16)[name = tensor("q_61_cast_fp16")]; + tensor var_4583 = const()[name = tensor("op_4583"), val = tensor([1, 1])]; + tensor var_4585 = const()[name = tensor("op_4585"), val = tensor([1, 1])]; + tensor k_61_pad_type_0 = const()[name = tensor("k_61_pad_type_0"), val = tensor("custom")]; + tensor k_61_pad_0 = const()[name = tensor("k_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833416064)))]; + tensor k_61_cast_fp16 = conv(dilations = var_4585, groups = var_3945, pad = k_61_pad_0, pad_type = k_61_pad_type_0, strides = var_4583, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_327_cast_fp16)[name = tensor("k_61_cast_fp16")]; + tensor var_4589 = const()[name = tensor("op_4589"), val = tensor([1, 1])]; + tensor var_4591 = const()[name = tensor("op_4591"), val = tensor([1, 1])]; + tensor v_61_pad_type_0 = const()[name = tensor("v_61_pad_type_0"), val = tensor("custom")]; + tensor v_61_pad_0 = const()[name = tensor("v_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833620928)))]; + tensor v_61_cast_fp16 = conv(dilations = var_4591, groups = var_3945, pad = v_61_pad_0, pad_type = v_61_pad_type_0, strides = var_4589, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_327_cast_fp16)[name = tensor("v_61_cast_fp16")]; + tensor var_4595 = const()[name = tensor("op_4595"), val = tensor([2, 5, 64, -1])]; + tensor var_4596_cast_fp16 = reshape(shape = var_4595, x = q_61_cast_fp16)[name = tensor("op_4596_cast_fp16")]; + tensor var_4597 = const()[name = tensor("op_4597"), val = tensor([2, 5, 64, -1])]; + tensor var_4598_cast_fp16 = reshape(shape = var_4597, x = k_61_cast_fp16)[name = tensor("op_4598_cast_fp16")]; + tensor var_4599 = const()[name = tensor("op_4599"), val = tensor([2, 5, 64, -1])]; + tensor var_4600_cast_fp16 = reshape(shape = var_4599, x = v_61_cast_fp16)[name = tensor("op_4600_cast_fp16")]; + tensor attn_weights_121_transpose_x_0 = const()[name = tensor("attn_weights_121_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_121_transpose_y_0 = const()[name = tensor("attn_weights_121_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_121_cast_fp16 = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = var_4596_cast_fp16, y = var_4598_cast_fp16)[name = tensor("attn_weights_121_cast_fp16")]; + tensor attn_weights_123_cast_fp16 = mul(x = attn_weights_121_cast_fp16, y = var_3936_to_fp16)[name = tensor("attn_weights_123_cast_fp16")]; + tensor var_4604_cast_fp16 = softmax(axis = var_3929, x = attn_weights_123_cast_fp16)[name = tensor("op_4604_cast_fp16")]; + tensor attn_61_transpose_x_0 = const()[name = tensor("attn_61_transpose_x_0"), val = tensor(false)]; + tensor attn_61_transpose_y_0 = const()[name = tensor("attn_61_transpose_y_0"), val = tensor(true)]; + tensor attn_61_cast_fp16 = matmul(transpose_x = attn_61_transpose_x_0, transpose_y = attn_61_transpose_y_0, x = var_4600_cast_fp16, y = var_4604_cast_fp16)[name = tensor("attn_61_cast_fp16")]; + tensor var_4608 = const()[name = tensor("op_4608"), val = tensor([2, 320, 1, -1])]; + tensor input_517_cast_fp16 = reshape(shape = var_4608, x = attn_61_cast_fp16)[name = tensor("input_517_cast_fp16")]; + tensor var_4613 = const()[name = tensor("op_4613"), val = tensor([1, 1])]; + tensor var_4615 = const()[name = tensor("op_4615"), val = tensor([1, 1])]; + tensor var_4617_pad_type_0 = const()[name = tensor("op_4617_pad_type_0"), val = tensor("custom")]; + tensor var_4617_pad_0 = const()[name = tensor("op_4617_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833825792)))]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(834030656)))]; + tensor var_4617_cast_fp16 = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_4615, groups = var_3945, pad = var_4617_pad_0, pad_type = var_4617_pad_type_0, strides = var_4613, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_517_cast_fp16)[name = tensor("op_4617_cast_fp16")]; + tensor inputs_93_cast_fp16 = add(x = var_4617_cast_fp16, y = inputs_91_cast_fp16)[name = tensor("inputs_93_cast_fp16")]; + tensor var_4621 = const()[name = tensor("op_4621"), val = tensor([1])]; + tensor channels_mean_93_cast_fp16 = reduce_mean(axes = var_4621, keep_dims = var_3940, x = inputs_93_cast_fp16)[name = tensor("channels_mean_93_cast_fp16")]; + tensor zero_mean_93_cast_fp16 = sub(x = inputs_93_cast_fp16, y = channels_mean_93_cast_fp16)[name = tensor("zero_mean_93_cast_fp16")]; + tensor zero_mean_sq_93_cast_fp16 = mul(x = zero_mean_93_cast_fp16, y = zero_mean_93_cast_fp16)[name = tensor("zero_mean_sq_93_cast_fp16")]; + tensor var_4625 = const()[name = tensor("op_4625"), val = tensor([1])]; + tensor var_4626_cast_fp16 = reduce_mean(axes = var_4625, keep_dims = var_3940, x = zero_mean_sq_93_cast_fp16)[name = tensor("op_4626_cast_fp16")]; + tensor var_4627_to_fp16 = const()[name = tensor("op_4627_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4628_cast_fp16 = add(x = var_4626_cast_fp16, y = var_4627_to_fp16)[name = tensor("op_4628_cast_fp16")]; + tensor denom_93_epsilon_0_to_fp16 = const()[name = tensor("denom_93_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_93_cast_fp16 = rsqrt(epsilon = denom_93_epsilon_0_to_fp16, x = var_4628_cast_fp16)[name = tensor("denom_93_cast_fp16")]; + tensor out_93_cast_fp16 = mul(x = zero_mean_93_cast_fp16, y = denom_93_cast_fp16)[name = tensor("out_93_cast_fp16")]; + tensor var_4632_to_fp16 = const()[name = tensor("op_4632_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(834031360)))]; + tensor var_4633_cast_fp16 = add(x = out_93_cast_fp16, y = var_4632_to_fp16)[name = tensor("op_4633_cast_fp16")]; + tensor var_4635_to_fp16 = const()[name = tensor("op_4635_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(834032064)))]; + tensor hidden_states_329_cast_fp16 = mul(x = var_4633_cast_fp16, y = var_4635_to_fp16)[name = tensor("hidden_states_329_cast_fp16")]; + tensor var_4642 = const()[name = tensor("op_4642"), val = tensor([1, 1])]; + tensor var_4644 = const()[name = tensor("op_4644"), val = tensor([1, 1])]; + tensor q_pad_type_0 = const()[name = tensor("q_pad_type_0"), val = tensor("custom")]; + tensor q_pad_0 = const()[name = tensor("q_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(834032768)))]; + tensor q_cast_fp16 = conv(dilations = var_4644, groups = var_3945, pad = q_pad_0, pad_type = q_pad_type_0, strides = var_4642, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_329_cast_fp16)[name = tensor("q_cast_fp16")]; + tensor var_4648 = const()[name = tensor("op_4648"), val = tensor([1, 1])]; + tensor var_4650 = const()[name = tensor("op_4650"), val = tensor([1, 1])]; + tensor k_pad_type_0 = const()[name = tensor("k_pad_type_0"), val = tensor("custom")]; + tensor k_pad_0 = const()[name = tensor("k_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(834237632)))]; + tensor k_cast_fp16 = conv(dilations = var_4650, groups = var_3945, pad = k_pad_0, pad_type = k_pad_type_0, strides = var_4648, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_cast_fp16")]; + tensor var_4654 = const()[name = tensor("op_4654"), val = tensor([1, 1])]; + tensor var_4656 = const()[name = tensor("op_4656"), val = tensor([1, 1])]; + tensor v_pad_type_0 = const()[name = tensor("v_pad_type_0"), val = tensor("custom")]; + tensor v_pad_0 = const()[name = tensor("v_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(834893056)))]; + tensor v_cast_fp16 = conv(dilations = var_4656, groups = var_3945, pad = v_pad_0, pad_type = v_pad_type_0, strides = var_4654, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_cast_fp16")]; + tensor var_4660 = const()[name = tensor("op_4660"), val = tensor([2, 5, 64, -1])]; + tensor var_4661_cast_fp16 = reshape(shape = var_4660, x = q_cast_fp16)[name = tensor("op_4661_cast_fp16")]; + tensor var_4662 = const()[name = tensor("op_4662"), val = tensor([2, 5, 64, -1])]; + tensor var_4663_cast_fp16 = reshape(shape = var_4662, x = k_cast_fp16)[name = tensor("op_4663_cast_fp16")]; + tensor var_4664 = const()[name = tensor("op_4664"), val = tensor([2, 5, 64, -1])]; + tensor var_4665_cast_fp16 = reshape(shape = var_4664, x = v_cast_fp16)[name = tensor("op_4665_cast_fp16")]; + tensor attn_weights_125_transpose_x_0 = const()[name = tensor("attn_weights_125_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_125_transpose_y_0 = const()[name = tensor("attn_weights_125_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_125_cast_fp16 = matmul(transpose_x = attn_weights_125_transpose_x_0, transpose_y = attn_weights_125_transpose_y_0, x = var_4661_cast_fp16, y = var_4663_cast_fp16)[name = tensor("attn_weights_125_cast_fp16")]; + tensor attn_weights_cast_fp16 = mul(x = attn_weights_125_cast_fp16, y = var_3936_to_fp16)[name = tensor("attn_weights_cast_fp16")]; + tensor var_4669_cast_fp16 = softmax(axis = var_3929, x = attn_weights_cast_fp16)[name = tensor("op_4669_cast_fp16")]; + tensor attn_transpose_x_0 = const()[name = tensor("attn_transpose_x_0"), val = tensor(false)]; + tensor attn_transpose_y_0 = const()[name = tensor("attn_transpose_y_0"), val = tensor(true)]; + tensor attn_cast_fp16 = matmul(transpose_x = attn_transpose_x_0, transpose_y = attn_transpose_y_0, x = var_4665_cast_fp16, y = var_4669_cast_fp16)[name = tensor("attn_cast_fp16")]; + tensor var_4673 = const()[name = tensor("op_4673"), val = tensor([2, 320, 1, -1])]; + tensor input_519_cast_fp16 = reshape(shape = var_4673, x = attn_cast_fp16)[name = tensor("input_519_cast_fp16")]; + tensor var_4678 = const()[name = tensor("op_4678"), val = tensor([1, 1])]; + tensor var_4680 = const()[name = tensor("op_4680"), val = tensor([1, 1])]; + tensor var_4682_pad_type_0 = const()[name = tensor("op_4682_pad_type_0"), val = tensor("custom")]; + tensor var_4682_pad_0 = const()[name = tensor("op_4682_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(835548480)))]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(835753344)))]; + tensor var_4682_cast_fp16 = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_4680, groups = var_3945, pad = var_4682_pad_0, pad_type = var_4682_pad_type_0, strides = var_4678, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_519_cast_fp16)[name = tensor("op_4682_cast_fp16")]; + tensor inputs_cast_fp16 = add(x = var_4682_cast_fp16, y = inputs_93_cast_fp16)[name = tensor("inputs_cast_fp16")]; + tensor var_4686 = const()[name = tensor("op_4686"), val = tensor([1])]; + tensor channels_mean_cast_fp16 = reduce_mean(axes = var_4686, keep_dims = var_3940, x = inputs_cast_fp16)[name = tensor("channels_mean_cast_fp16")]; + tensor zero_mean_cast_fp16 = sub(x = inputs_cast_fp16, y = channels_mean_cast_fp16)[name = tensor("zero_mean_cast_fp16")]; + tensor zero_mean_sq_cast_fp16 = mul(x = zero_mean_cast_fp16, y = zero_mean_cast_fp16)[name = tensor("zero_mean_sq_cast_fp16")]; + tensor var_4690 = const()[name = tensor("op_4690"), val = tensor([1])]; + tensor var_4691_cast_fp16 = reduce_mean(axes = var_4690, keep_dims = var_3940, x = zero_mean_sq_cast_fp16)[name = tensor("op_4691_cast_fp16")]; + tensor var_4692_to_fp16 = const()[name = tensor("op_4692_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4693_cast_fp16 = add(x = var_4691_cast_fp16, y = var_4692_to_fp16)[name = tensor("op_4693_cast_fp16")]; + tensor denom_epsilon_0_to_fp16 = const()[name = tensor("denom_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_cast_fp16 = rsqrt(epsilon = denom_epsilon_0_to_fp16, x = var_4693_cast_fp16)[name = tensor("denom_cast_fp16")]; + tensor out_cast_fp16 = mul(x = zero_mean_cast_fp16, y = denom_cast_fp16)[name = tensor("out_cast_fp16")]; + tensor var_4697_to_fp16 = const()[name = tensor("op_4697_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(835754048)))]; + tensor var_4698_cast_fp16 = add(x = out_cast_fp16, y = var_4697_to_fp16)[name = tensor("op_4698_cast_fp16")]; + tensor var_4700_to_fp16 = const()[name = tensor("op_4700_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(835754752)))]; + tensor input_521_cast_fp16 = mul(x = var_4698_cast_fp16, y = var_4700_to_fp16)[name = tensor("input_521_cast_fp16")]; + tensor var_4708 = const()[name = tensor("op_4708"), val = tensor([1, 1])]; + tensor var_4710 = const()[name = tensor("op_4710"), val = tensor([1, 1])]; + tensor var_4712_pad_type_0 = const()[name = tensor("op_4712_pad_type_0"), val = tensor("custom")]; + tensor var_4712_pad_0 = const()[name = tensor("op_4712_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(835755456)))]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(837393920)))]; + tensor var_4712_cast_fp16 = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_4710, groups = var_3945, pad = var_4712_pad_0, pad_type = var_4712_pad_type_0, strides = var_4708, weight = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_521_cast_fp16)[name = tensor("op_4712_cast_fp16")]; + tensor var_4713_split_sizes_0 = const()[name = tensor("op_4713_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_4713_axis_0 = const()[name = tensor("op_4713_axis_0"), val = tensor(1)]; + tensor var_4713_cast_fp16_0, tensor var_4713_cast_fp16_1 = split(axis = var_4713_axis_0, split_sizes = var_4713_split_sizes_0, x = var_4712_cast_fp16)[name = tensor("op_4713_cast_fp16")]; + tensor var_4715_mode_0 = const()[name = tensor("op_4715_mode_0"), val = tensor("EXACT")]; + tensor var_4715_cast_fp16 = gelu(mode = var_4715_mode_0, x = var_4713_cast_fp16_1)[name = tensor("op_4715_cast_fp16")]; + tensor input_523_cast_fp16 = mul(x = var_4713_cast_fp16_0, y = var_4715_cast_fp16)[name = tensor("input_523_cast_fp16")]; + tensor var_4719 = const()[name = tensor("op_4719"), val = tensor([1, 1])]; + tensor var_4721 = const()[name = tensor("op_4721"), val = tensor([1, 1])]; + tensor var_4723_pad_type_0 = const()[name = tensor("op_4723_pad_type_0"), val = tensor("custom")]; + tensor var_4723_pad_0 = const()[name = tensor("op_4723_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(837399104)))]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(838218368)))]; + tensor var_4723_cast_fp16 = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_4721, groups = var_3945, pad = var_4723_pad_0, pad_type = var_4723_pad_type_0, strides = var_4719, weight = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_523_cast_fp16)[name = tensor("op_4723_cast_fp16")]; + tensor hidden_states_333_cast_fp16 = add(x = var_4723_cast_fp16, y = inputs_cast_fp16)[name = tensor("hidden_states_333_cast_fp16")]; + tensor var_4725 = const()[name = tensor("op_4725"), val = tensor([2, 320, 48, 80])]; + tensor input_525_cast_fp16 = reshape(shape = var_4725, x = hidden_states_333_cast_fp16)[name = tensor("input_525_cast_fp16")]; + tensor var_4729 = const()[name = tensor("op_4729"), val = tensor([1, 1])]; + tensor var_4731 = const()[name = tensor("op_4731"), val = tensor([1, 1])]; + tensor hidden_states_pad_type_0 = const()[name = tensor("hidden_states_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_pad_0 = const()[name = tensor("hidden_states_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_proj_out_weight_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(838219072)))]; + tensor up_blocks_3_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(838423936)))]; + tensor hidden_states_cast_fp16 = conv(bias = up_blocks_3_attentions_2_proj_out_bias_to_fp16, dilations = var_4731, groups = var_3945, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_4729, weight = up_blocks_3_attentions_2_proj_out_weight_to_fp16, x = input_525_cast_fp16)[name = tensor("hidden_states_cast_fp16")]; + tensor input_527_cast_fp16 = add(x = hidden_states_cast_fp16, y = hidden_states_323_cast_fp16)[name = tensor("input_527_cast_fp16")]; + tensor reshape_240_shape_0 = const()[name = tensor("reshape_240_shape_0"), val = tensor([2, 32, 10, 48, 80])]; + tensor reshape_240_cast_fp16 = reshape(shape = reshape_240_shape_0, x = input_527_cast_fp16)[name = tensor("reshape_240_cast_fp16")]; + tensor reduce_mean_180_axes_0 = const()[name = tensor("reduce_mean_180_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_180_keep_dims_0 = const()[name = tensor("reduce_mean_180_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_180_cast_fp16 = reduce_mean(axes = reduce_mean_180_axes_0, keep_dims = reduce_mean_180_keep_dims_0, x = reshape_240_cast_fp16)[name = tensor("reduce_mean_180_cast_fp16")]; + tensor sub_120_cast_fp16 = sub(x = reshape_240_cast_fp16, y = reduce_mean_180_cast_fp16)[name = tensor("sub_120_cast_fp16")]; + tensor square_60_cast_fp16 = square(x = sub_120_cast_fp16)[name = tensor("square_60_cast_fp16")]; + tensor reduce_mean_182_axes_0 = const()[name = tensor("reduce_mean_182_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_182_keep_dims_0 = const()[name = tensor("reduce_mean_182_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_182_cast_fp16 = reduce_mean(axes = reduce_mean_182_axes_0, keep_dims = reduce_mean_182_keep_dims_0, x = square_60_cast_fp16)[name = tensor("reduce_mean_182_cast_fp16")]; + tensor add_120_y_0_to_fp16 = const()[name = tensor("add_120_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_120_cast_fp16 = add(x = reduce_mean_182_cast_fp16, y = add_120_y_0_to_fp16)[name = tensor("add_120_cast_fp16")]; + tensor sqrt_60_cast_fp16 = sqrt(x = add_120_cast_fp16)[name = tensor("sqrt_60_cast_fp16")]; + tensor real_div_60_cast_fp16 = real_div(x = sub_120_cast_fp16, y = sqrt_60_cast_fp16)[name = tensor("real_div_60_cast_fp16")]; + tensor reshape_241_shape_0 = const()[name = tensor("reshape_241_shape_0"), val = tensor([2, 320, 48, 80])]; + tensor reshape_241_cast_fp16 = reshape(shape = reshape_241_shape_0, x = real_div_60_cast_fp16)[name = tensor("reshape_241_cast_fp16")]; + tensor add_121_gamma_0_to_fp16 = const()[name = tensor("add_121_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(838424640)))]; + tensor add_121_beta_0_to_fp16 = const()[name = tensor("add_121_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(838425344)))]; + tensor add_121_epsilon_0_to_fp16 = const()[name = tensor("add_121_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_121_cast_fp16 = batch_norm(beta = add_121_beta_0_to_fp16, epsilon = add_121_epsilon_0_to_fp16, gamma = add_121_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_241_cast_fp16)[name = tensor("add_121_cast_fp16")]; + tensor input_cast_fp16 = silu(x = add_121_cast_fp16)[name = tensor("input_cast_fp16")]; + tensor var_4745 = const()[name = tensor("op_4745"), val = tensor(1)]; + tensor var_4748 = const()[name = tensor("op_4748"), val = tensor([1, 1])]; + tensor var_4750 = const()[name = tensor("op_4750"), val = tensor([1, 1])]; + tensor var_4752_pad_type_0 = const()[name = tensor("op_4752_pad_type_0"), val = tensor("custom")]; + tensor var_4752_pad_0 = const()[name = tensor("op_4752_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv_out_weight_to_fp16 = const()[name = tensor("conv_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(838426048)))]; + tensor conv_out_bias_to_fp16 = const()[name = tensor("conv_out_bias_to_fp16"), val = tensor([-0x1.4b4p-9, 0x1.6f4p-9, 0x1.9ap-12, 0x1.04p-9])]; + tensor var_4752_cast_fp16 = conv(bias = conv_out_bias_to_fp16, dilations = var_4750, groups = var_4745, pad = var_4752_pad_0, pad_type = var_4752_pad_type_0, strides = var_4748, weight = conv_out_weight_to_fp16, x = input_cast_fp16)[name = tensor("op_4752_cast_fp16")]; + tensor var_4752_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("op_4752_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor noise_pred = cast(dtype = var_4752_cast_fp16_to_fp32_dtype_0, x = var_4752_cast_fp16)[name = tensor("cast_0")]; + } -> (noise_pred); +} \ No newline at end of file