diff --git "a/SplitEinsum-Resources-6bit/VAEDecoder.mlmodelc/model.mil" "b/SplitEinsum-Resources-6bit/VAEDecoder.mlmodelc/model.mil" new file mode 100644--- /dev/null +++ "b/SplitEinsum-Resources-6bit/VAEDecoder.mlmodelc/model.mil" @@ -0,0 +1,963 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3304.5.2"}, {"coremlc-version", "3304.6.2"}, {"coremltools-component-torch", "2.2.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.0b1"}})] +{ + func main(tensor z) { + tensor post_quant_conv_bias = const()[name = tensor("post_quant_conv_bias"), val = tensor([-0x1.d8p-5, 0x1.dp-3, -0x1.c6p-4, 0x1.acp-3])]; + tensor post_quant_conv_weight = const()[name = tensor("post_quant_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor decoder_conv_in_bias = const()[name = tensor("decoder_conv_in_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192)))]; + tensor decoder_conv_in_weight = const()[name = tensor("decoder_conv_in_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2304)))]; + tensor decoder_mid_block_resnets_0_conv1_bias = const()[name = tensor("decoder_mid_block_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76096)))]; + tensor decoder_mid_block_resnets_0_conv1_weight = const()[name = tensor("decoder_mid_block_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78208)))]; + tensor decoder_mid_block_resnets_0_conv2_bias = const()[name = tensor("decoder_mid_block_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9515456)))]; + tensor decoder_mid_block_resnets_0_conv2_weight = const()[name = tensor("decoder_mid_block_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9517568)))]; + tensor decoder_mid_block_attentions_0_to_q_bias = const()[name = tensor("decoder_mid_block_attentions_0_to_q_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18954816)))]; + tensor decoder_mid_block_attentions_0_to_q_weight = const()[name = tensor("decoder_mid_block_attentions_0_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18956928)))]; + tensor decoder_mid_block_attentions_0_to_k_bias = const()[name = tensor("decoder_mid_block_attentions_0_to_k_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20005568)))]; + tensor decoder_mid_block_attentions_0_to_k_weight = const()[name = tensor("decoder_mid_block_attentions_0_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20007680)))]; + tensor decoder_mid_block_attentions_0_to_v_bias = const()[name = tensor("decoder_mid_block_attentions_0_to_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21056320)))]; + tensor decoder_mid_block_attentions_0_to_v_weight = const()[name = tensor("decoder_mid_block_attentions_0_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21058432)))]; + tensor decoder_mid_block_attentions_0_to_out_0_bias = const()[name = tensor("decoder_mid_block_attentions_0_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22107072)))]; + tensor decoder_mid_block_attentions_0_to_out_0_weight = const()[name = tensor("decoder_mid_block_attentions_0_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22109184)))]; + tensor decoder_mid_block_resnets_1_conv1_bias = const()[name = tensor("decoder_mid_block_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23157824)))]; + tensor decoder_mid_block_resnets_1_conv1_weight = const()[name = tensor("decoder_mid_block_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23159936)))]; + tensor decoder_mid_block_resnets_1_conv2_bias = const()[name = tensor("decoder_mid_block_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32597184)))]; + tensor decoder_mid_block_resnets_1_conv2_weight = const()[name = tensor("decoder_mid_block_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32599296)))]; + tensor decoder_up_blocks_0_resnets_0_conv1_bias = const()[name = tensor("decoder_up_blocks_0_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42036544)))]; + tensor decoder_up_blocks_0_resnets_0_conv1_weight = const()[name = tensor("decoder_up_blocks_0_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42038656)))]; + tensor decoder_up_blocks_0_resnets_0_conv2_bias = const()[name = tensor("decoder_up_blocks_0_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51475904)))]; + tensor decoder_up_blocks_0_resnets_0_conv2_weight = const()[name = tensor("decoder_up_blocks_0_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51478016)))]; + tensor decoder_up_blocks_0_resnets_1_conv1_bias = const()[name = tensor("decoder_up_blocks_0_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60915264)))]; + tensor decoder_up_blocks_0_resnets_1_conv1_weight = const()[name = tensor("decoder_up_blocks_0_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60917376)))]; + tensor decoder_up_blocks_0_resnets_1_conv2_bias = const()[name = tensor("decoder_up_blocks_0_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70354624)))]; + tensor decoder_up_blocks_0_resnets_1_conv2_weight = const()[name = tensor("decoder_up_blocks_0_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70356736)))]; + tensor decoder_up_blocks_0_resnets_2_conv1_bias = const()[name = tensor("decoder_up_blocks_0_resnets_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79793984)))]; + tensor decoder_up_blocks_0_resnets_2_conv1_weight = const()[name = tensor("decoder_up_blocks_0_resnets_2_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79796096)))]; + tensor decoder_up_blocks_0_resnets_2_conv2_bias = const()[name = tensor("decoder_up_blocks_0_resnets_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89233344)))]; + tensor decoder_up_blocks_0_resnets_2_conv2_weight = const()[name = tensor("decoder_up_blocks_0_resnets_2_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89235456)))]; + tensor decoder_up_blocks_0_upsamplers_0_conv_bias = const()[name = tensor("decoder_up_blocks_0_upsamplers_0_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98672704)))]; + tensor decoder_up_blocks_0_upsamplers_0_conv_weight = const()[name = tensor("decoder_up_blocks_0_upsamplers_0_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98674816)))]; + tensor decoder_up_blocks_1_resnets_0_conv1_bias = const()[name = tensor("decoder_up_blocks_1_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108112064)))]; + tensor decoder_up_blocks_1_resnets_0_conv1_weight = const()[name = tensor("decoder_up_blocks_1_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108114176)))]; + tensor decoder_up_blocks_1_resnets_0_conv2_bias = const()[name = tensor("decoder_up_blocks_1_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117551424)))]; + tensor decoder_up_blocks_1_resnets_0_conv2_weight = const()[name = tensor("decoder_up_blocks_1_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117553536)))]; + tensor decoder_up_blocks_1_resnets_1_conv1_bias = const()[name = tensor("decoder_up_blocks_1_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126990784)))]; + tensor decoder_up_blocks_1_resnets_1_conv1_weight = const()[name = tensor("decoder_up_blocks_1_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126992896)))]; + tensor decoder_up_blocks_1_resnets_1_conv2_bias = const()[name = tensor("decoder_up_blocks_1_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136430144)))]; + tensor decoder_up_blocks_1_resnets_1_conv2_weight = const()[name = tensor("decoder_up_blocks_1_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136432256)))]; + tensor decoder_up_blocks_1_resnets_2_conv1_bias = const()[name = tensor("decoder_up_blocks_1_resnets_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145869504)))]; + tensor decoder_up_blocks_1_resnets_2_conv1_weight = const()[name = tensor("decoder_up_blocks_1_resnets_2_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145871616)))]; + tensor decoder_up_blocks_1_resnets_2_conv2_bias = const()[name = tensor("decoder_up_blocks_1_resnets_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(155308864)))]; + tensor decoder_up_blocks_1_resnets_2_conv2_weight = const()[name = tensor("decoder_up_blocks_1_resnets_2_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(155310976)))]; + tensor decoder_up_blocks_1_upsamplers_0_conv_bias = const()[name = tensor("decoder_up_blocks_1_upsamplers_0_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(164748224)))]; + tensor decoder_up_blocks_1_upsamplers_0_conv_weight = const()[name = tensor("decoder_up_blocks_1_upsamplers_0_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(164750336)))]; + tensor decoder_up_blocks_2_resnets_0_conv1_bias = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(174187584)))]; + tensor decoder_up_blocks_2_resnets_0_conv1_weight = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(174188672)))]; + tensor decoder_up_blocks_2_resnets_0_conv2_bias = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178907328)))]; + tensor decoder_up_blocks_2_resnets_0_conv2_weight = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178908416)))]; + tensor decoder_up_blocks_2_resnets_0_conv_shortcut_bias = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv_shortcut_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181267776)))]; + tensor decoder_up_blocks_2_resnets_0_conv_shortcut_weight = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv_shortcut_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181268864)))]; + tensor decoder_up_blocks_2_resnets_1_conv1_bias = const()[name = tensor("decoder_up_blocks_2_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181793216)))]; + tensor decoder_up_blocks_2_resnets_1_conv1_weight = const()[name = tensor("decoder_up_blocks_2_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181794304)))]; + tensor decoder_up_blocks_2_resnets_1_conv2_bias = const()[name = tensor("decoder_up_blocks_2_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184153664)))]; + tensor decoder_up_blocks_2_resnets_1_conv2_weight = const()[name = tensor("decoder_up_blocks_2_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184154752)))]; + tensor decoder_up_blocks_2_resnets_2_conv1_bias = const()[name = tensor("decoder_up_blocks_2_resnets_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(186514112)))]; + tensor decoder_up_blocks_2_resnets_2_conv1_weight = const()[name = tensor("decoder_up_blocks_2_resnets_2_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(186515200)))]; + tensor decoder_up_blocks_2_resnets_2_conv2_bias = const()[name = tensor("decoder_up_blocks_2_resnets_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188874560)))]; + tensor decoder_up_blocks_2_resnets_2_conv2_weight = const()[name = tensor("decoder_up_blocks_2_resnets_2_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188875648)))]; + tensor decoder_up_blocks_2_upsamplers_0_conv_bias = const()[name = tensor("decoder_up_blocks_2_upsamplers_0_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(191235008)))]; + tensor decoder_up_blocks_2_upsamplers_0_conv_weight = const()[name = tensor("decoder_up_blocks_2_upsamplers_0_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(191236096)))]; + tensor decoder_up_blocks_3_resnets_0_conv1_bias = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193595456)))]; + tensor decoder_up_blocks_3_resnets_0_conv1_weight = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193596032)))]; + tensor decoder_up_blocks_3_resnets_0_conv2_bias = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194775744)))]; + tensor decoder_up_blocks_3_resnets_0_conv2_weight = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194776320)))]; + tensor decoder_up_blocks_3_resnets_0_conv_shortcut_bias = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv_shortcut_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195366208)))]; + tensor decoder_up_blocks_3_resnets_0_conv_shortcut_weight = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv_shortcut_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195366784)))]; + tensor decoder_up_blocks_3_resnets_1_conv1_bias = const()[name = tensor("decoder_up_blocks_3_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195497920)))]; + tensor decoder_up_blocks_3_resnets_1_conv1_weight = const()[name = tensor("decoder_up_blocks_3_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195498496)))]; + tensor decoder_up_blocks_3_resnets_1_conv2_bias = const()[name = tensor("decoder_up_blocks_3_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196088384)))]; + tensor decoder_up_blocks_3_resnets_1_conv2_weight = const()[name = tensor("decoder_up_blocks_3_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196088960)))]; + tensor decoder_up_blocks_3_resnets_2_conv1_bias = const()[name = tensor("decoder_up_blocks_3_resnets_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196678848)))]; + tensor decoder_up_blocks_3_resnets_2_conv1_weight = const()[name = tensor("decoder_up_blocks_3_resnets_2_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196679424)))]; + tensor decoder_up_blocks_3_resnets_2_conv2_bias = const()[name = tensor("decoder_up_blocks_3_resnets_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197269312)))]; + tensor decoder_up_blocks_3_resnets_2_conv2_weight = const()[name = tensor("decoder_up_blocks_3_resnets_2_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197269888)))]; + tensor decoder_conv_out_bias = const()[name = tensor("decoder_conv_out_bias"), val = tensor([0x1.f8p-4, 0x1.5p-4, 0x1.a2p-5])]; + tensor decoder_conv_out_weight = const()[name = tensor("decoder_conv_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197859776)))]; + tensor var_7 = const()[name = tensor("op_7"), val = tensor(1)]; + tensor var_10 = const()[name = tensor("op_10"), val = tensor([1, 1])]; + tensor var_12 = const()[name = tensor("op_12"), val = tensor([1, 1])]; + tensor input_1_pad_type_0 = const()[name = tensor("input_1_pad_type_0"), val = tensor("custom")]; + tensor input_1_pad_0 = const()[name = tensor("input_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_1 = conv(bias = post_quant_conv_bias, dilations = var_12, groups = var_7, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = var_10, weight = post_quant_conv_weight, x = z)[name = tensor("input_1")]; + tensor var_26 = const()[name = tensor("op_26"), val = tensor(1)]; + tensor var_44 = const()[name = tensor("op_44"), val = tensor([1, 1])]; + tensor var_46 = const()[name = tensor("op_46"), val = tensor([1, 1])]; + tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("custom")]; + tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_3 = conv(bias = decoder_conv_in_bias, dilations = var_46, groups = var_26, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_44, weight = decoder_conv_in_weight, x = input_1)[name = tensor("input_3")]; + tensor reshape_0_shape_0 = const()[name = tensor("reshape_0_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_0 = reshape(shape = reshape_0_shape_0, x = input_3)[name = tensor("reshape_0")]; + tensor reduce_mean_0_axes_0 = const()[name = tensor("reduce_mean_0_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_0_keep_dims_0 = const()[name = tensor("reduce_mean_0_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_0 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0)[name = tensor("reduce_mean_0")]; + tensor sub_0 = sub(x = reshape_0, y = reduce_mean_0)[name = tensor("sub_0")]; + tensor square_0 = square(x = sub_0)[name = tensor("square_0")]; + tensor reduce_mean_2_axes_0 = const()[name = tensor("reduce_mean_2_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_2_keep_dims_0 = const()[name = tensor("reduce_mean_2_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_2 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0)[name = tensor("reduce_mean_2")]; + tensor add_0_y_0 = const()[name = tensor("add_0_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_0 = add(x = reduce_mean_2, y = add_0_y_0)[name = tensor("add_0")]; + tensor sqrt_0 = sqrt(x = add_0)[name = tensor("sqrt_0")]; + tensor real_div_0 = real_div(x = sub_0, y = sqrt_0)[name = tensor("real_div_0")]; + tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_1 = reshape(shape = reshape_1_shape_0, x = real_div_0)[name = tensor("reshape_1")]; + tensor add_1_mean_0 = const()[name = tensor("add_1_mean_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197873664)))]; + tensor add_1_variance_0 = const()[name = tensor("add_1_variance_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197875776)))]; + tensor add_1_gamma_0 = const()[name = tensor("add_1_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197877888)))]; + tensor add_1_beta_0 = const()[name = tensor("add_1_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197880000)))]; + tensor add_1_epsilon_0 = const()[name = tensor("add_1_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_1 = batch_norm(beta = add_1_beta_0, epsilon = add_1_epsilon_0, gamma = add_1_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_1)[name = tensor("add_1")]; + tensor input_7 = silu(x = add_1)[name = tensor("input_7")]; + tensor var_65 = const()[name = tensor("op_65"), val = tensor([1, 1])]; + tensor var_67 = const()[name = tensor("op_67"), val = tensor([1, 1])]; + tensor input_9_pad_type_0 = const()[name = tensor("input_9_pad_type_0"), val = tensor("custom")]; + tensor input_9_pad_0 = const()[name = tensor("input_9_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_9 = conv(bias = decoder_mid_block_resnets_0_conv1_bias, dilations = var_67, groups = var_26, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = var_65, weight = decoder_mid_block_resnets_0_conv1_weight, x = input_7)[name = tensor("input_9")]; + tensor reshape_4_shape_0 = const()[name = tensor("reshape_4_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_4 = reshape(shape = reshape_4_shape_0, x = input_9)[name = tensor("reshape_4")]; + tensor reduce_mean_3_axes_0 = const()[name = tensor("reduce_mean_3_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_3_keep_dims_0 = const()[name = tensor("reduce_mean_3_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_3 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4)[name = tensor("reduce_mean_3")]; + tensor sub_2 = sub(x = reshape_4, y = reduce_mean_3)[name = tensor("sub_2")]; + tensor square_1 = square(x = sub_2)[name = tensor("square_1")]; + tensor reduce_mean_5_axes_0 = const()[name = tensor("reduce_mean_5_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_5_keep_dims_0 = const()[name = tensor("reduce_mean_5_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_5 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1)[name = tensor("reduce_mean_5")]; + tensor add_2_y_0 = const()[name = tensor("add_2_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_2 = add(x = reduce_mean_5, y = add_2_y_0)[name = tensor("add_2")]; + tensor sqrt_1 = sqrt(x = add_2)[name = tensor("sqrt_1")]; + tensor real_div_1 = real_div(x = sub_2, y = sqrt_1)[name = tensor("real_div_1")]; + tensor reshape_5_shape_0 = const()[name = tensor("reshape_5_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_5 = reshape(shape = reshape_5_shape_0, x = real_div_1)[name = tensor("reshape_5")]; + tensor add_3_gamma_0 = const()[name = tensor("add_3_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197882112)))]; + tensor add_3_beta_0 = const()[name = tensor("add_3_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197884224)))]; + tensor add_3_epsilon_0 = const()[name = tensor("add_3_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_3 = batch_norm(beta = add_3_beta_0, epsilon = add_3_epsilon_0, gamma = add_3_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_5)[name = tensor("add_3")]; + tensor input_13 = silu(x = add_3)[name = tensor("input_13")]; + tensor var_77 = const()[name = tensor("op_77"), val = tensor([1, 1])]; + tensor var_79 = const()[name = tensor("op_79"), val = tensor([1, 1])]; + tensor hidden_states_1_pad_type_0 = const()[name = tensor("hidden_states_1_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_1_pad_0 = const()[name = tensor("hidden_states_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_1 = conv(bias = decoder_mid_block_resnets_0_conv2_bias, dilations = var_79, groups = var_26, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_77, weight = decoder_mid_block_resnets_0_conv2_weight, x = input_13)[name = tensor("hidden_states_1")]; + tensor var_82 = add(x = input_3, y = hidden_states_1)[name = tensor("op_82")]; + tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([1, 32, 16, 16384])]; + tensor reshape_8 = reshape(shape = reshape_8_shape_0, x = var_82)[name = tensor("reshape_8")]; + tensor reduce_mean_6_axes_0 = const()[name = tensor("reduce_mean_6_axes_0"), val = tensor([2, 3])]; + tensor reduce_mean_6_keep_dims_0 = const()[name = tensor("reduce_mean_6_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_6 = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8)[name = tensor("reduce_mean_6")]; + tensor sub_4 = sub(x = reshape_8, y = reduce_mean_6)[name = tensor("sub_4")]; + tensor square_2 = square(x = sub_4)[name = tensor("square_2")]; + tensor reduce_mean_8_axes_0 = const()[name = tensor("reduce_mean_8_axes_0"), val = tensor([2, 3])]; + tensor reduce_mean_8_keep_dims_0 = const()[name = tensor("reduce_mean_8_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_8 = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2)[name = tensor("reduce_mean_8")]; + tensor add_4_y_0 = const()[name = tensor("add_4_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_4 = add(x = reduce_mean_8, y = add_4_y_0)[name = tensor("add_4")]; + tensor sqrt_2 = sqrt(x = add_4)[name = tensor("sqrt_2")]; + tensor real_div_2 = real_div(x = sub_4, y = sqrt_2)[name = tensor("real_div_2")]; + tensor reshape_9_shape_0 = const()[name = tensor("reshape_9_shape_0"), val = tensor([1, 512, 16384])]; + tensor reshape_9 = reshape(shape = reshape_9_shape_0, x = real_div_2)[name = tensor("reshape_9")]; + tensor reshape_10 = const()[name = tensor("reshape_10"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197886336)))]; + tensor mul_2 = mul(x = reshape_9, y = reshape_10)[name = tensor("mul_2")]; + tensor reshape_11 = const()[name = tensor("reshape_11"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197888448)))]; + tensor add_5 = add(x = mul_2, y = reshape_11)[name = tensor("add_5")]; + tensor input_19_perm_0 = const()[name = tensor("input_19_perm_0"), val = tensor([0, 2, 1])]; + tensor input_19 = transpose(perm = input_19_perm_0, x = add_5)[name = tensor("transpose_11")]; + tensor linear_0 = linear(bias = decoder_mid_block_attentions_0_to_q_bias, weight = decoder_mid_block_attentions_0_to_q_weight, x = input_19)[name = tensor("linear_0")]; + tensor linear_1 = linear(bias = decoder_mid_block_attentions_0_to_k_bias, weight = decoder_mid_block_attentions_0_to_k_weight, x = input_19)[name = tensor("linear_1")]; + tensor linear_2 = linear(bias = decoder_mid_block_attentions_0_to_v_bias, weight = decoder_mid_block_attentions_0_to_v_weight, x = input_19)[name = tensor("linear_2")]; + tensor var_123 = const()[name = tensor("op_123"), val = tensor([1, -1, 1, 512])]; + tensor var_124 = reshape(shape = var_123, x = linear_0)[name = tensor("op_124")]; + tensor var_126 = const()[name = tensor("op_126"), val = tensor([1, -1, 1, 512])]; + tensor var_127 = reshape(shape = var_126, x = linear_1)[name = tensor("op_127")]; + tensor var_129 = const()[name = tensor("op_129"), val = tensor([1, -1, 1, 512])]; + tensor var_130 = reshape(shape = var_129, x = linear_2)[name = tensor("op_130")]; + tensor value_perm_0 = const()[name = tensor("value_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor mul_3_y_0 = const()[name = tensor("mul_3_y_0"), val = tensor(0x1.6a09e6p-5)]; + tensor mul_3 = mul(x = var_124, y = mul_3_y_0)[name = tensor("mul_3")]; + tensor matmul_0_transpose_y_0 = const()[name = tensor("matmul_0_transpose_y_0"), val = tensor(true)]; + tensor matmul_0_transpose_x_0 = const()[name = tensor("matmul_0_transpose_x_0"), val = tensor(false)]; + tensor transpose_4_perm_0 = const()[name = tensor("transpose_4_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_5_perm_0 = const()[name = tensor("transpose_5_perm_0"), val = tensor([0, 2, -3, -1])]; + tensor transpose_5 = transpose(perm = transpose_5_perm_0, x = var_127)[name = tensor("transpose_8")]; + tensor transpose_4 = transpose(perm = transpose_4_perm_0, x = mul_3)[name = tensor("transpose_9")]; + tensor matmul_0 = matmul(transpose_x = matmul_0_transpose_x_0, transpose_y = matmul_0_transpose_y_0, x = transpose_4, y = transpose_5)[name = tensor("matmul_0")]; + tensor softmax_0_axis_0 = const()[name = tensor("softmax_0_axis_0"), val = tensor(-1)]; + tensor softmax_0 = softmax(axis = softmax_0_axis_0, x = matmul_0)[name = tensor("softmax_0")]; + tensor hidden_states_7_transpose_x_0 = const()[name = tensor("hidden_states_7_transpose_x_0"), val = tensor(false)]; + tensor hidden_states_7_transpose_y_0 = const()[name = tensor("hidden_states_7_transpose_y_0"), val = tensor(false)]; + tensor value = transpose(perm = value_perm_0, x = var_130)[name = tensor("transpose_10")]; + tensor hidden_states_7 = matmul(transpose_x = hidden_states_7_transpose_x_0, transpose_y = hidden_states_7_transpose_y_0, x = softmax_0, y = value)[name = tensor("hidden_states_7")]; + tensor var_133_perm_0 = const()[name = tensor("op_133_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_137 = const()[name = tensor("op_137"), val = tensor([1, -1, 512])]; + tensor var_133 = transpose(perm = var_133_perm_0, x = hidden_states_7)[name = tensor("transpose_7")]; + tensor hidden_states_9 = reshape(shape = var_137, x = var_133)[name = tensor("hidden_states_9")]; + tensor linear_3 = linear(bias = decoder_mid_block_attentions_0_to_out_0_bias, weight = decoder_mid_block_attentions_0_to_out_0_weight, x = hidden_states_9)[name = tensor("linear_3")]; + tensor var_144_perm_0 = const()[name = tensor("op_144_perm_0"), val = tensor([0, -1, -2])]; + tensor var_145 = const()[name = tensor("op_145"), val = tensor([1, 512, 128, 128])]; + tensor var_144 = transpose(perm = var_144_perm_0, x = linear_3)[name = tensor("transpose_6")]; + tensor hidden_states_13 = reshape(shape = var_145, x = var_144)[name = tensor("hidden_states_13")]; + tensor hidden_states_15 = add(x = hidden_states_13, y = var_82)[name = tensor("hidden_states_15")]; + tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_12 = reshape(shape = reshape_12_shape_0, x = hidden_states_15)[name = tensor("reshape_12")]; + tensor reduce_mean_9_axes_0 = const()[name = tensor("reduce_mean_9_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_9_keep_dims_0 = const()[name = tensor("reduce_mean_9_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_9 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12)[name = tensor("reduce_mean_9")]; + tensor sub_6 = sub(x = reshape_12, y = reduce_mean_9)[name = tensor("sub_6")]; + tensor square_3 = square(x = sub_6)[name = tensor("square_3")]; + tensor reduce_mean_11_axes_0 = const()[name = tensor("reduce_mean_11_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_11_keep_dims_0 = const()[name = tensor("reduce_mean_11_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_11 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3)[name = tensor("reduce_mean_11")]; + tensor add_6_y_0 = const()[name = tensor("add_6_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_6 = add(x = reduce_mean_11, y = add_6_y_0)[name = tensor("add_6")]; + tensor sqrt_3 = sqrt(x = add_6)[name = tensor("sqrt_3")]; + tensor real_div_3 = real_div(x = sub_6, y = sqrt_3)[name = tensor("real_div_3")]; + tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_13 = reshape(shape = reshape_13_shape_0, x = real_div_3)[name = tensor("reshape_13")]; + tensor add_7_gamma_0 = const()[name = tensor("add_7_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197890560)))]; + tensor add_7_beta_0 = const()[name = tensor("add_7_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197892672)))]; + tensor add_7_epsilon_0 = const()[name = tensor("add_7_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_7 = batch_norm(beta = add_7_beta_0, epsilon = add_7_epsilon_0, gamma = add_7_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_13)[name = tensor("add_7")]; + tensor input_29 = silu(x = add_7)[name = tensor("input_29")]; + tensor var_160 = const()[name = tensor("op_160"), val = tensor([1, 1])]; + tensor var_162 = const()[name = tensor("op_162"), val = tensor([1, 1])]; + tensor input_31_pad_type_0 = const()[name = tensor("input_31_pad_type_0"), val = tensor("custom")]; + tensor input_31_pad_0 = const()[name = tensor("input_31_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_31 = conv(bias = decoder_mid_block_resnets_1_conv1_bias, dilations = var_162, groups = var_26, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = var_160, weight = decoder_mid_block_resnets_1_conv1_weight, x = input_29)[name = tensor("input_31")]; + tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_16 = reshape(shape = reshape_16_shape_0, x = input_31)[name = tensor("reshape_16")]; + tensor reduce_mean_12_axes_0 = const()[name = tensor("reduce_mean_12_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_12_keep_dims_0 = const()[name = tensor("reduce_mean_12_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_12 = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16)[name = tensor("reduce_mean_12")]; + tensor sub_8 = sub(x = reshape_16, y = reduce_mean_12)[name = tensor("sub_8")]; + tensor square_4 = square(x = sub_8)[name = tensor("square_4")]; + tensor reduce_mean_14_axes_0 = const()[name = tensor("reduce_mean_14_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_14_keep_dims_0 = const()[name = tensor("reduce_mean_14_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_14 = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4)[name = tensor("reduce_mean_14")]; + tensor add_8_y_0 = const()[name = tensor("add_8_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_8 = add(x = reduce_mean_14, y = add_8_y_0)[name = tensor("add_8")]; + tensor sqrt_4 = sqrt(x = add_8)[name = tensor("sqrt_4")]; + tensor real_div_4 = real_div(x = sub_8, y = sqrt_4)[name = tensor("real_div_4")]; + tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_17 = reshape(shape = reshape_17_shape_0, x = real_div_4)[name = tensor("reshape_17")]; + tensor add_9_gamma_0 = const()[name = tensor("add_9_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197894784)))]; + tensor add_9_beta_0 = const()[name = tensor("add_9_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197896896)))]; + tensor add_9_epsilon_0 = const()[name = tensor("add_9_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_9 = batch_norm(beta = add_9_beta_0, epsilon = add_9_epsilon_0, gamma = add_9_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_17)[name = tensor("add_9")]; + tensor input_35 = silu(x = add_9)[name = tensor("input_35")]; + tensor var_172 = const()[name = tensor("op_172"), val = tensor([1, 1])]; + tensor var_174 = const()[name = tensor("op_174"), val = tensor([1, 1])]; + tensor hidden_states_17_pad_type_0 = const()[name = tensor("hidden_states_17_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_17_pad_0 = const()[name = tensor("hidden_states_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_17 = conv(bias = decoder_mid_block_resnets_1_conv2_bias, dilations = var_174, groups = var_26, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = var_172, weight = decoder_mid_block_resnets_1_conv2_weight, x = input_35)[name = tensor("hidden_states_17")]; + tensor var_177 = add(x = hidden_states_15, y = hidden_states_17)[name = tensor("op_177")]; + tensor reshape_20_shape_0 = const()[name = tensor("reshape_20_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_20 = reshape(shape = reshape_20_shape_0, x = var_177)[name = tensor("reshape_20")]; + tensor reduce_mean_15_axes_0 = const()[name = tensor("reduce_mean_15_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_15_keep_dims_0 = const()[name = tensor("reduce_mean_15_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_15 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20)[name = tensor("reduce_mean_15")]; + tensor sub_10 = sub(x = reshape_20, y = reduce_mean_15)[name = tensor("sub_10")]; + tensor square_5 = square(x = sub_10)[name = tensor("square_5")]; + tensor reduce_mean_17_axes_0 = const()[name = tensor("reduce_mean_17_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_17_keep_dims_0 = const()[name = tensor("reduce_mean_17_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_17 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5)[name = tensor("reduce_mean_17")]; + tensor add_10_y_0 = const()[name = tensor("add_10_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_10 = add(x = reduce_mean_17, y = add_10_y_0)[name = tensor("add_10")]; + tensor sqrt_5 = sqrt(x = add_10)[name = tensor("sqrt_5")]; + tensor real_div_5 = real_div(x = sub_10, y = sqrt_5)[name = tensor("real_div_5")]; + tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_21 = reshape(shape = reshape_21_shape_0, x = real_div_5)[name = tensor("reshape_21")]; + tensor add_11_gamma_0 = const()[name = tensor("add_11_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197899008)))]; + tensor add_11_beta_0 = const()[name = tensor("add_11_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197901120)))]; + tensor add_11_epsilon_0 = const()[name = tensor("add_11_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_11 = batch_norm(beta = add_11_beta_0, epsilon = add_11_epsilon_0, gamma = add_11_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_21)[name = tensor("add_11")]; + tensor input_43 = silu(x = add_11)[name = tensor("input_43")]; + tensor var_199 = const()[name = tensor("op_199"), val = tensor([1, 1])]; + tensor var_201 = const()[name = tensor("op_201"), val = tensor([1, 1])]; + tensor input_45_pad_type_0 = const()[name = tensor("input_45_pad_type_0"), val = tensor("custom")]; + tensor input_45_pad_0 = const()[name = tensor("input_45_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_45 = conv(bias = decoder_up_blocks_0_resnets_0_conv1_bias, dilations = var_201, groups = var_26, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = var_199, weight = decoder_up_blocks_0_resnets_0_conv1_weight, x = input_43)[name = tensor("input_45")]; + tensor reshape_24_shape_0 = const()[name = tensor("reshape_24_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_24 = reshape(shape = reshape_24_shape_0, x = input_45)[name = tensor("reshape_24")]; + tensor reduce_mean_18_axes_0 = const()[name = tensor("reduce_mean_18_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_18_keep_dims_0 = const()[name = tensor("reduce_mean_18_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_18 = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24)[name = tensor("reduce_mean_18")]; + tensor sub_12 = sub(x = reshape_24, y = reduce_mean_18)[name = tensor("sub_12")]; + tensor square_6 = square(x = sub_12)[name = tensor("square_6")]; + tensor reduce_mean_20_axes_0 = const()[name = tensor("reduce_mean_20_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_20_keep_dims_0 = const()[name = tensor("reduce_mean_20_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_20 = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6)[name = tensor("reduce_mean_20")]; + tensor add_12_y_0 = const()[name = tensor("add_12_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_12 = add(x = reduce_mean_20, y = add_12_y_0)[name = tensor("add_12")]; + tensor sqrt_6 = sqrt(x = add_12)[name = tensor("sqrt_6")]; + tensor real_div_6 = real_div(x = sub_12, y = sqrt_6)[name = tensor("real_div_6")]; + tensor reshape_25_shape_0 = const()[name = tensor("reshape_25_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_25 = reshape(shape = reshape_25_shape_0, x = real_div_6)[name = tensor("reshape_25")]; + tensor add_13_gamma_0 = const()[name = tensor("add_13_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197903232)))]; + tensor add_13_beta_0 = const()[name = tensor("add_13_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197905344)))]; + tensor add_13_epsilon_0 = const()[name = tensor("add_13_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_13 = batch_norm(beta = add_13_beta_0, epsilon = add_13_epsilon_0, gamma = add_13_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_25)[name = tensor("add_13")]; + tensor input_49 = silu(x = add_13)[name = tensor("input_49")]; + tensor var_211 = const()[name = tensor("op_211"), val = tensor([1, 1])]; + tensor var_213 = const()[name = tensor("op_213"), val = tensor([1, 1])]; + tensor hidden_states_19_pad_type_0 = const()[name = tensor("hidden_states_19_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_19_pad_0 = const()[name = tensor("hidden_states_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_19 = conv(bias = decoder_up_blocks_0_resnets_0_conv2_bias, dilations = var_213, groups = var_26, pad = hidden_states_19_pad_0, pad_type = hidden_states_19_pad_type_0, strides = var_211, weight = decoder_up_blocks_0_resnets_0_conv2_weight, x = input_49)[name = tensor("hidden_states_19")]; + tensor var_216 = add(x = var_177, y = hidden_states_19)[name = tensor("op_216")]; + tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_28 = reshape(shape = reshape_28_shape_0, x = var_216)[name = tensor("reshape_28")]; + tensor reduce_mean_21_axes_0 = const()[name = tensor("reduce_mean_21_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_21_keep_dims_0 = const()[name = tensor("reduce_mean_21_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_21 = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28)[name = tensor("reduce_mean_21")]; + tensor sub_14 = sub(x = reshape_28, y = reduce_mean_21)[name = tensor("sub_14")]; + tensor square_7 = square(x = sub_14)[name = tensor("square_7")]; + tensor reduce_mean_23_axes_0 = const()[name = tensor("reduce_mean_23_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_23_keep_dims_0 = const()[name = tensor("reduce_mean_23_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_23 = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7)[name = tensor("reduce_mean_23")]; + tensor add_14_y_0 = const()[name = tensor("add_14_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_14 = add(x = reduce_mean_23, y = add_14_y_0)[name = tensor("add_14")]; + tensor sqrt_7 = sqrt(x = add_14)[name = tensor("sqrt_7")]; + tensor real_div_7 = real_div(x = sub_14, y = sqrt_7)[name = tensor("real_div_7")]; + tensor reshape_29_shape_0 = const()[name = tensor("reshape_29_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_29 = reshape(shape = reshape_29_shape_0, x = real_div_7)[name = tensor("reshape_29")]; + tensor add_15_gamma_0 = const()[name = tensor("add_15_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197907456)))]; + tensor add_15_beta_0 = const()[name = tensor("add_15_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197909568)))]; + tensor add_15_epsilon_0 = const()[name = tensor("add_15_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_15 = batch_norm(beta = add_15_beta_0, epsilon = add_15_epsilon_0, gamma = add_15_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_29)[name = tensor("add_15")]; + tensor input_57 = silu(x = add_15)[name = tensor("input_57")]; + tensor var_229 = const()[name = tensor("op_229"), val = tensor([1, 1])]; + tensor var_231 = const()[name = tensor("op_231"), val = tensor([1, 1])]; + tensor input_59_pad_type_0 = const()[name = tensor("input_59_pad_type_0"), val = tensor("custom")]; + tensor input_59_pad_0 = const()[name = tensor("input_59_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_59 = conv(bias = decoder_up_blocks_0_resnets_1_conv1_bias, dilations = var_231, groups = var_26, pad = input_59_pad_0, pad_type = input_59_pad_type_0, strides = var_229, weight = decoder_up_blocks_0_resnets_1_conv1_weight, x = input_57)[name = tensor("input_59")]; + tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_32 = reshape(shape = reshape_32_shape_0, x = input_59)[name = tensor("reshape_32")]; + tensor reduce_mean_24_axes_0 = const()[name = tensor("reduce_mean_24_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_24_keep_dims_0 = const()[name = tensor("reduce_mean_24_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_24 = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32)[name = tensor("reduce_mean_24")]; + tensor sub_16 = sub(x = reshape_32, y = reduce_mean_24)[name = tensor("sub_16")]; + tensor square_8 = square(x = sub_16)[name = tensor("square_8")]; + tensor reduce_mean_26_axes_0 = const()[name = tensor("reduce_mean_26_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_26_keep_dims_0 = const()[name = tensor("reduce_mean_26_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_26 = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8)[name = tensor("reduce_mean_26")]; + tensor add_16_y_0 = const()[name = tensor("add_16_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_16 = add(x = reduce_mean_26, y = add_16_y_0)[name = tensor("add_16")]; + tensor sqrt_8 = sqrt(x = add_16)[name = tensor("sqrt_8")]; + tensor real_div_8 = real_div(x = sub_16, y = sqrt_8)[name = tensor("real_div_8")]; + tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_33 = reshape(shape = reshape_33_shape_0, x = real_div_8)[name = tensor("reshape_33")]; + tensor add_17_gamma_0 = const()[name = tensor("add_17_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197911680)))]; + tensor add_17_beta_0 = const()[name = tensor("add_17_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197913792)))]; + tensor add_17_epsilon_0 = const()[name = tensor("add_17_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_17 = batch_norm(beta = add_17_beta_0, epsilon = add_17_epsilon_0, gamma = add_17_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_33)[name = tensor("add_17")]; + tensor input_63 = silu(x = add_17)[name = tensor("input_63")]; + tensor var_241 = const()[name = tensor("op_241"), val = tensor([1, 1])]; + tensor var_243 = const()[name = tensor("op_243"), val = tensor([1, 1])]; + tensor hidden_states_21_pad_type_0 = const()[name = tensor("hidden_states_21_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_21_pad_0 = const()[name = tensor("hidden_states_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_21 = conv(bias = decoder_up_blocks_0_resnets_1_conv2_bias, dilations = var_243, groups = var_26, pad = hidden_states_21_pad_0, pad_type = hidden_states_21_pad_type_0, strides = var_241, weight = decoder_up_blocks_0_resnets_1_conv2_weight, x = input_63)[name = tensor("hidden_states_21")]; + tensor var_246 = add(x = var_216, y = hidden_states_21)[name = tensor("op_246")]; + tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_36 = reshape(shape = reshape_36_shape_0, x = var_246)[name = tensor("reshape_36")]; + tensor reduce_mean_27_axes_0 = const()[name = tensor("reduce_mean_27_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_27_keep_dims_0 = const()[name = tensor("reduce_mean_27_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_27 = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36)[name = tensor("reduce_mean_27")]; + tensor sub_18 = sub(x = reshape_36, y = reduce_mean_27)[name = tensor("sub_18")]; + tensor square_9 = square(x = sub_18)[name = tensor("square_9")]; + tensor reduce_mean_29_axes_0 = const()[name = tensor("reduce_mean_29_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_29_keep_dims_0 = const()[name = tensor("reduce_mean_29_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_29 = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9)[name = tensor("reduce_mean_29")]; + tensor add_18_y_0 = const()[name = tensor("add_18_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_18 = add(x = reduce_mean_29, y = add_18_y_0)[name = tensor("add_18")]; + tensor sqrt_9 = sqrt(x = add_18)[name = tensor("sqrt_9")]; + tensor real_div_9 = real_div(x = sub_18, y = sqrt_9)[name = tensor("real_div_9")]; + tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_37 = reshape(shape = reshape_37_shape_0, x = real_div_9)[name = tensor("reshape_37")]; + tensor add_19_gamma_0 = const()[name = tensor("add_19_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197915904)))]; + tensor add_19_beta_0 = const()[name = tensor("add_19_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197918016)))]; + tensor add_19_epsilon_0 = const()[name = tensor("add_19_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_19 = batch_norm(beta = add_19_beta_0, epsilon = add_19_epsilon_0, gamma = add_19_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_37)[name = tensor("add_19")]; + tensor input_71 = silu(x = add_19)[name = tensor("input_71")]; + tensor var_259 = const()[name = tensor("op_259"), val = tensor([1, 1])]; + tensor var_261 = const()[name = tensor("op_261"), val = tensor([1, 1])]; + tensor input_73_pad_type_0 = const()[name = tensor("input_73_pad_type_0"), val = tensor("custom")]; + tensor input_73_pad_0 = const()[name = tensor("input_73_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_73 = conv(bias = decoder_up_blocks_0_resnets_2_conv1_bias, dilations = var_261, groups = var_26, pad = input_73_pad_0, pad_type = input_73_pad_type_0, strides = var_259, weight = decoder_up_blocks_0_resnets_2_conv1_weight, x = input_71)[name = tensor("input_73")]; + tensor reshape_40_shape_0 = const()[name = tensor("reshape_40_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_40 = reshape(shape = reshape_40_shape_0, x = input_73)[name = tensor("reshape_40")]; + tensor reduce_mean_30_axes_0 = const()[name = tensor("reduce_mean_30_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_30_keep_dims_0 = const()[name = tensor("reduce_mean_30_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_30 = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40)[name = tensor("reduce_mean_30")]; + tensor sub_20 = sub(x = reshape_40, y = reduce_mean_30)[name = tensor("sub_20")]; + tensor square_10 = square(x = sub_20)[name = tensor("square_10")]; + tensor reduce_mean_32_axes_0 = const()[name = tensor("reduce_mean_32_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_32_keep_dims_0 = const()[name = tensor("reduce_mean_32_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_32 = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10)[name = tensor("reduce_mean_32")]; + tensor add_20_y_0 = const()[name = tensor("add_20_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_20 = add(x = reduce_mean_32, y = add_20_y_0)[name = tensor("add_20")]; + tensor sqrt_10 = sqrt(x = add_20)[name = tensor("sqrt_10")]; + tensor real_div_10 = real_div(x = sub_20, y = sqrt_10)[name = tensor("real_div_10")]; + tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_41 = reshape(shape = reshape_41_shape_0, x = real_div_10)[name = tensor("reshape_41")]; + tensor add_21_gamma_0 = const()[name = tensor("add_21_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197920128)))]; + tensor add_21_beta_0 = const()[name = tensor("add_21_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197922240)))]; + tensor add_21_epsilon_0 = const()[name = tensor("add_21_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_21 = batch_norm(beta = add_21_beta_0, epsilon = add_21_epsilon_0, gamma = add_21_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_41)[name = tensor("add_21")]; + tensor input_77 = silu(x = add_21)[name = tensor("input_77")]; + tensor var_271 = const()[name = tensor("op_271"), val = tensor([1, 1])]; + tensor var_273 = const()[name = tensor("op_273"), val = tensor([1, 1])]; + tensor hidden_states_23_pad_type_0 = const()[name = tensor("hidden_states_23_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_23_pad_0 = const()[name = tensor("hidden_states_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_23 = conv(bias = decoder_up_blocks_0_resnets_2_conv2_bias, dilations = var_273, groups = var_26, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = var_271, weight = decoder_up_blocks_0_resnets_2_conv2_weight, x = input_77)[name = tensor("hidden_states_23")]; + tensor var_276 = add(x = var_246, y = hidden_states_23)[name = tensor("op_276")]; + tensor input_81_scale_factor_height_0 = const()[name = tensor("input_81_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_81_scale_factor_width_0 = const()[name = tensor("input_81_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_81 = upsample_nearest_neighbor(scale_factor_height = input_81_scale_factor_height_0, scale_factor_width = input_81_scale_factor_width_0, x = var_276)[name = tensor("input_81")]; + tensor var_284 = const()[name = tensor("op_284"), val = tensor([1, 1])]; + tensor var_286 = const()[name = tensor("op_286"), val = tensor([1, 1])]; + tensor input_83_pad_type_0 = const()[name = tensor("input_83_pad_type_0"), val = tensor("custom")]; + tensor input_83_pad_0 = const()[name = tensor("input_83_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_83 = conv(bias = decoder_up_blocks_0_upsamplers_0_conv_bias, dilations = var_286, groups = var_26, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = var_284, weight = decoder_up_blocks_0_upsamplers_0_conv_weight, x = input_81)[name = tensor("input_83")]; + tensor reshape_44_shape_0 = const()[name = tensor("reshape_44_shape_0"), val = tensor([1, 32, 16, 256, 256])]; + tensor reshape_44 = reshape(shape = reshape_44_shape_0, x = input_83)[name = tensor("reshape_44")]; + tensor reduce_mean_33_axes_0 = const()[name = tensor("reduce_mean_33_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_33_keep_dims_0 = const()[name = tensor("reduce_mean_33_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_33 = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44)[name = tensor("reduce_mean_33")]; + tensor sub_22 = sub(x = reshape_44, y = reduce_mean_33)[name = tensor("sub_22")]; + tensor square_11 = square(x = sub_22)[name = tensor("square_11")]; + tensor reduce_mean_35_axes_0 = const()[name = tensor("reduce_mean_35_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_35_keep_dims_0 = const()[name = tensor("reduce_mean_35_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_35 = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11)[name = tensor("reduce_mean_35")]; + tensor add_22_y_0 = const()[name = tensor("add_22_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_22 = add(x = reduce_mean_35, y = add_22_y_0)[name = tensor("add_22")]; + tensor sqrt_11 = sqrt(x = add_22)[name = tensor("sqrt_11")]; + tensor real_div_11 = real_div(x = sub_22, y = sqrt_11)[name = tensor("real_div_11")]; + tensor reshape_45_shape_0 = const()[name = tensor("reshape_45_shape_0"), val = tensor([1, 512, 256, 256])]; + tensor reshape_45 = reshape(shape = reshape_45_shape_0, x = real_div_11)[name = tensor("reshape_45")]; + tensor add_23_gamma_0 = const()[name = tensor("add_23_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197924352)))]; + tensor add_23_beta_0 = const()[name = tensor("add_23_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197926464)))]; + tensor add_23_epsilon_0 = const()[name = tensor("add_23_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_23 = batch_norm(beta = add_23_beta_0, epsilon = add_23_epsilon_0, gamma = add_23_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_45)[name = tensor("add_23")]; + tensor input_87 = silu(x = add_23)[name = tensor("input_87")]; + tensor var_307 = const()[name = tensor("op_307"), val = tensor([1, 1])]; + tensor var_309 = const()[name = tensor("op_309"), val = tensor([1, 1])]; + tensor input_89_pad_type_0 = const()[name = tensor("input_89_pad_type_0"), val = tensor("custom")]; + tensor input_89_pad_0 = const()[name = tensor("input_89_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_89 = conv(bias = decoder_up_blocks_1_resnets_0_conv1_bias, dilations = var_309, groups = var_26, pad = input_89_pad_0, pad_type = input_89_pad_type_0, strides = var_307, weight = decoder_up_blocks_1_resnets_0_conv1_weight, x = input_87)[name = tensor("input_89")]; + tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([1, 32, 16, 256, 256])]; + tensor reshape_48 = reshape(shape = reshape_48_shape_0, x = input_89)[name = tensor("reshape_48")]; + tensor reduce_mean_36_axes_0 = const()[name = tensor("reduce_mean_36_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_36_keep_dims_0 = const()[name = tensor("reduce_mean_36_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_36 = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48)[name = tensor("reduce_mean_36")]; + tensor sub_24 = sub(x = reshape_48, y = reduce_mean_36)[name = tensor("sub_24")]; + tensor square_12 = square(x = sub_24)[name = tensor("square_12")]; + tensor reduce_mean_38_axes_0 = const()[name = tensor("reduce_mean_38_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_38_keep_dims_0 = const()[name = tensor("reduce_mean_38_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_38 = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12)[name = tensor("reduce_mean_38")]; + tensor add_24_y_0 = const()[name = tensor("add_24_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_24 = add(x = reduce_mean_38, y = add_24_y_0)[name = tensor("add_24")]; + tensor sqrt_12 = sqrt(x = add_24)[name = tensor("sqrt_12")]; + tensor real_div_12 = real_div(x = sub_24, y = sqrt_12)[name = tensor("real_div_12")]; + tensor reshape_49_shape_0 = const()[name = tensor("reshape_49_shape_0"), val = tensor([1, 512, 256, 256])]; + tensor reshape_49 = reshape(shape = reshape_49_shape_0, x = real_div_12)[name = tensor("reshape_49")]; + tensor add_25_gamma_0 = const()[name = tensor("add_25_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197928576)))]; + tensor add_25_beta_0 = const()[name = tensor("add_25_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197930688)))]; + tensor add_25_epsilon_0 = const()[name = tensor("add_25_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_25 = batch_norm(beta = add_25_beta_0, epsilon = add_25_epsilon_0, gamma = add_25_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_49)[name = tensor("add_25")]; + tensor input_93 = silu(x = add_25)[name = tensor("input_93")]; + tensor var_319 = const()[name = tensor("op_319"), val = tensor([1, 1])]; + tensor var_321 = const()[name = tensor("op_321"), val = tensor([1, 1])]; + tensor hidden_states_27_pad_type_0 = const()[name = tensor("hidden_states_27_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_27_pad_0 = const()[name = tensor("hidden_states_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_27 = conv(bias = decoder_up_blocks_1_resnets_0_conv2_bias, dilations = var_321, groups = var_26, pad = hidden_states_27_pad_0, pad_type = hidden_states_27_pad_type_0, strides = var_319, weight = decoder_up_blocks_1_resnets_0_conv2_weight, x = input_93)[name = tensor("hidden_states_27")]; + tensor var_324 = add(x = input_83, y = hidden_states_27)[name = tensor("op_324")]; + tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([1, 32, 16, 256, 256])]; + tensor reshape_52 = reshape(shape = reshape_52_shape_0, x = var_324)[name = tensor("reshape_52")]; + tensor reduce_mean_39_axes_0 = const()[name = tensor("reduce_mean_39_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_39_keep_dims_0 = const()[name = tensor("reduce_mean_39_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_39 = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52)[name = tensor("reduce_mean_39")]; + tensor sub_26 = sub(x = reshape_52, y = reduce_mean_39)[name = tensor("sub_26")]; + tensor square_13 = square(x = sub_26)[name = tensor("square_13")]; + tensor reduce_mean_41_axes_0 = const()[name = tensor("reduce_mean_41_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_41_keep_dims_0 = const()[name = tensor("reduce_mean_41_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_41 = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13)[name = tensor("reduce_mean_41")]; + tensor add_26_y_0 = const()[name = tensor("add_26_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_26 = add(x = reduce_mean_41, y = add_26_y_0)[name = tensor("add_26")]; + tensor sqrt_13 = sqrt(x = add_26)[name = tensor("sqrt_13")]; + tensor real_div_13 = real_div(x = sub_26, y = sqrt_13)[name = tensor("real_div_13")]; + tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([1, 512, 256, 256])]; + tensor reshape_53 = reshape(shape = reshape_53_shape_0, x = real_div_13)[name = tensor("reshape_53")]; + tensor add_27_gamma_0 = const()[name = tensor("add_27_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197932800)))]; + tensor add_27_beta_0 = const()[name = tensor("add_27_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197934912)))]; + tensor add_27_epsilon_0 = const()[name = tensor("add_27_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_27 = batch_norm(beta = add_27_beta_0, epsilon = add_27_epsilon_0, gamma = add_27_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_53)[name = tensor("add_27")]; + tensor input_101 = silu(x = add_27)[name = tensor("input_101")]; + tensor var_337 = const()[name = tensor("op_337"), val = tensor([1, 1])]; + tensor var_339 = const()[name = tensor("op_339"), val = tensor([1, 1])]; + tensor input_103_pad_type_0 = const()[name = tensor("input_103_pad_type_0"), val = tensor("custom")]; + tensor input_103_pad_0 = const()[name = tensor("input_103_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_103 = conv(bias = decoder_up_blocks_1_resnets_1_conv1_bias, dilations = var_339, groups = var_26, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = var_337, weight = decoder_up_blocks_1_resnets_1_conv1_weight, x = input_101)[name = tensor("input_103")]; + tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([1, 32, 16, 256, 256])]; + tensor reshape_56 = reshape(shape = reshape_56_shape_0, x = input_103)[name = tensor("reshape_56")]; + tensor reduce_mean_42_axes_0 = const()[name = tensor("reduce_mean_42_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_42_keep_dims_0 = const()[name = tensor("reduce_mean_42_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_42 = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56)[name = tensor("reduce_mean_42")]; + tensor sub_28 = sub(x = reshape_56, y = reduce_mean_42)[name = tensor("sub_28")]; + tensor square_14 = square(x = sub_28)[name = tensor("square_14")]; + tensor reduce_mean_44_axes_0 = const()[name = tensor("reduce_mean_44_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_44_keep_dims_0 = const()[name = tensor("reduce_mean_44_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_44 = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14)[name = tensor("reduce_mean_44")]; + tensor add_28_y_0 = const()[name = tensor("add_28_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_28 = add(x = reduce_mean_44, y = add_28_y_0)[name = tensor("add_28")]; + tensor sqrt_14 = sqrt(x = add_28)[name = tensor("sqrt_14")]; + tensor real_div_14 = real_div(x = sub_28, y = sqrt_14)[name = tensor("real_div_14")]; + tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([1, 512, 256, 256])]; + tensor reshape_57 = reshape(shape = reshape_57_shape_0, x = real_div_14)[name = tensor("reshape_57")]; + tensor add_29_gamma_0 = const()[name = tensor("add_29_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197937024)))]; + tensor add_29_beta_0 = const()[name = tensor("add_29_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197939136)))]; + tensor add_29_epsilon_0 = const()[name = tensor("add_29_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_29 = batch_norm(beta = add_29_beta_0, epsilon = add_29_epsilon_0, gamma = add_29_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_57)[name = tensor("add_29")]; + tensor input_107 = silu(x = add_29)[name = tensor("input_107")]; + tensor var_349 = const()[name = tensor("op_349"), val = tensor([1, 1])]; + tensor var_351 = const()[name = tensor("op_351"), val = tensor([1, 1])]; + tensor hidden_states_29_pad_type_0 = const()[name = tensor("hidden_states_29_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_29_pad_0 = const()[name = tensor("hidden_states_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_29 = conv(bias = decoder_up_blocks_1_resnets_1_conv2_bias, dilations = var_351, groups = var_26, pad = hidden_states_29_pad_0, pad_type = hidden_states_29_pad_type_0, strides = var_349, weight = decoder_up_blocks_1_resnets_1_conv2_weight, x = input_107)[name = tensor("hidden_states_29")]; + tensor var_354 = add(x = var_324, y = hidden_states_29)[name = tensor("op_354")]; + tensor reshape_60_shape_0 = const()[name = tensor("reshape_60_shape_0"), val = tensor([1, 32, 16, 256, 256])]; + tensor reshape_60 = reshape(shape = reshape_60_shape_0, x = var_354)[name = tensor("reshape_60")]; + tensor reduce_mean_45_axes_0 = const()[name = tensor("reduce_mean_45_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_45_keep_dims_0 = const()[name = tensor("reduce_mean_45_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_45 = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60)[name = tensor("reduce_mean_45")]; + tensor sub_30 = sub(x = reshape_60, y = reduce_mean_45)[name = tensor("sub_30")]; + tensor square_15 = square(x = sub_30)[name = tensor("square_15")]; + tensor reduce_mean_47_axes_0 = const()[name = tensor("reduce_mean_47_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_47_keep_dims_0 = const()[name = tensor("reduce_mean_47_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_47 = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15)[name = tensor("reduce_mean_47")]; + tensor add_30_y_0 = const()[name = tensor("add_30_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_30 = add(x = reduce_mean_47, y = add_30_y_0)[name = tensor("add_30")]; + tensor sqrt_15 = sqrt(x = add_30)[name = tensor("sqrt_15")]; + tensor real_div_15 = real_div(x = sub_30, y = sqrt_15)[name = tensor("real_div_15")]; + tensor reshape_61_shape_0 = const()[name = tensor("reshape_61_shape_0"), val = tensor([1, 512, 256, 256])]; + tensor reshape_61 = reshape(shape = reshape_61_shape_0, x = real_div_15)[name = tensor("reshape_61")]; + tensor add_31_gamma_0 = const()[name = tensor("add_31_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197941248)))]; + tensor add_31_beta_0 = const()[name = tensor("add_31_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197943360)))]; + tensor add_31_epsilon_0 = const()[name = tensor("add_31_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_31 = batch_norm(beta = add_31_beta_0, epsilon = add_31_epsilon_0, gamma = add_31_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_61)[name = tensor("add_31")]; + tensor input_115 = silu(x = add_31)[name = tensor("input_115")]; + tensor var_367 = const()[name = tensor("op_367"), val = tensor([1, 1])]; + tensor var_369 = const()[name = tensor("op_369"), val = tensor([1, 1])]; + tensor input_117_pad_type_0 = const()[name = tensor("input_117_pad_type_0"), val = tensor("custom")]; + tensor input_117_pad_0 = const()[name = tensor("input_117_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_117 = conv(bias = decoder_up_blocks_1_resnets_2_conv1_bias, dilations = var_369, groups = var_26, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = var_367, weight = decoder_up_blocks_1_resnets_2_conv1_weight, x = input_115)[name = tensor("input_117")]; + tensor reshape_64_shape_0 = const()[name = tensor("reshape_64_shape_0"), val = tensor([1, 32, 16, 256, 256])]; + tensor reshape_64 = reshape(shape = reshape_64_shape_0, x = input_117)[name = tensor("reshape_64")]; + tensor reduce_mean_48_axes_0 = const()[name = tensor("reduce_mean_48_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_48_keep_dims_0 = const()[name = tensor("reduce_mean_48_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_48 = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64)[name = tensor("reduce_mean_48")]; + tensor sub_32 = sub(x = reshape_64, y = reduce_mean_48)[name = tensor("sub_32")]; + tensor square_16 = square(x = sub_32)[name = tensor("square_16")]; + tensor reduce_mean_50_axes_0 = const()[name = tensor("reduce_mean_50_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_50_keep_dims_0 = const()[name = tensor("reduce_mean_50_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_50 = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16)[name = tensor("reduce_mean_50")]; + tensor add_32_y_0 = const()[name = tensor("add_32_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_32 = add(x = reduce_mean_50, y = add_32_y_0)[name = tensor("add_32")]; + tensor sqrt_16 = sqrt(x = add_32)[name = tensor("sqrt_16")]; + tensor real_div_16 = real_div(x = sub_32, y = sqrt_16)[name = tensor("real_div_16")]; + tensor reshape_65_shape_0 = const()[name = tensor("reshape_65_shape_0"), val = tensor([1, 512, 256, 256])]; + tensor reshape_65 = reshape(shape = reshape_65_shape_0, x = real_div_16)[name = tensor("reshape_65")]; + tensor add_33_gamma_0 = const()[name = tensor("add_33_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197945472)))]; + tensor add_33_beta_0 = const()[name = tensor("add_33_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197947584)))]; + tensor add_33_epsilon_0 = const()[name = tensor("add_33_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_33 = batch_norm(beta = add_33_beta_0, epsilon = add_33_epsilon_0, gamma = add_33_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_65)[name = tensor("add_33")]; + tensor input_121 = silu(x = add_33)[name = tensor("input_121")]; + tensor var_379 = const()[name = tensor("op_379"), val = tensor([1, 1])]; + tensor var_381 = const()[name = tensor("op_381"), val = tensor([1, 1])]; + tensor hidden_states_31_pad_type_0 = const()[name = tensor("hidden_states_31_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_31_pad_0 = const()[name = tensor("hidden_states_31_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_31 = conv(bias = decoder_up_blocks_1_resnets_2_conv2_bias, dilations = var_381, groups = var_26, pad = hidden_states_31_pad_0, pad_type = hidden_states_31_pad_type_0, strides = var_379, weight = decoder_up_blocks_1_resnets_2_conv2_weight, x = input_121)[name = tensor("hidden_states_31")]; + tensor var_384 = add(x = var_354, y = hidden_states_31)[name = tensor("op_384")]; + tensor input_125_scale_factor_height_0 = const()[name = tensor("input_125_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_125_scale_factor_width_0 = const()[name = tensor("input_125_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_125 = upsample_nearest_neighbor(scale_factor_height = input_125_scale_factor_height_0, scale_factor_width = input_125_scale_factor_width_0, x = var_384)[name = tensor("input_125")]; + tensor var_392 = const()[name = tensor("op_392"), val = tensor([1, 1])]; + tensor var_394 = const()[name = tensor("op_394"), val = tensor([1, 1])]; + tensor input_127_pad_type_0 = const()[name = tensor("input_127_pad_type_0"), val = tensor("custom")]; + tensor input_127_pad_0 = const()[name = tensor("input_127_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_127 = conv(bias = decoder_up_blocks_1_upsamplers_0_conv_bias, dilations = var_394, groups = var_26, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = var_392, weight = decoder_up_blocks_1_upsamplers_0_conv_weight, x = input_125)[name = tensor("input_127")]; + tensor reshape_68_shape_0 = const()[name = tensor("reshape_68_shape_0"), val = tensor([1, 32, 16, 512, 512])]; + tensor reshape_68 = reshape(shape = reshape_68_shape_0, x = input_127)[name = tensor("reshape_68")]; + tensor reduce_mean_51_axes_0 = const()[name = tensor("reduce_mean_51_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_51_keep_dims_0 = const()[name = tensor("reduce_mean_51_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_51 = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68)[name = tensor("reduce_mean_51")]; + tensor sub_34 = sub(x = reshape_68, y = reduce_mean_51)[name = tensor("sub_34")]; + tensor square_17 = square(x = sub_34)[name = tensor("square_17")]; + tensor reduce_mean_53_axes_0 = const()[name = tensor("reduce_mean_53_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_53_keep_dims_0 = const()[name = tensor("reduce_mean_53_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_53 = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17)[name = tensor("reduce_mean_53")]; + tensor add_34_y_0 = const()[name = tensor("add_34_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_34 = add(x = reduce_mean_53, y = add_34_y_0)[name = tensor("add_34")]; + tensor sqrt_17 = sqrt(x = add_34)[name = tensor("sqrt_17")]; + tensor real_div_17 = real_div(x = sub_34, y = sqrt_17)[name = tensor("real_div_17")]; + tensor reshape_69_shape_0 = const()[name = tensor("reshape_69_shape_0"), val = tensor([1, 512, 512, 512])]; + tensor reshape_69 = reshape(shape = reshape_69_shape_0, x = real_div_17)[name = tensor("reshape_69")]; + tensor add_35_gamma_0 = const()[name = tensor("add_35_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197949696)))]; + tensor add_35_beta_0 = const()[name = tensor("add_35_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197951808)))]; + tensor add_35_epsilon_0 = const()[name = tensor("add_35_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_35 = batch_norm(beta = add_35_beta_0, epsilon = add_35_epsilon_0, gamma = add_35_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_69)[name = tensor("add_35")]; + tensor input_131 = silu(x = add_35)[name = tensor("input_131")]; + tensor var_416 = const()[name = tensor("op_416"), val = tensor([1, 1])]; + tensor var_418 = const()[name = tensor("op_418"), val = tensor([1, 1])]; + tensor input_133_pad_type_0 = const()[name = tensor("input_133_pad_type_0"), val = tensor("custom")]; + tensor input_133_pad_0 = const()[name = tensor("input_133_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_133 = conv(bias = decoder_up_blocks_2_resnets_0_conv1_bias, dilations = var_418, groups = var_26, pad = input_133_pad_0, pad_type = input_133_pad_type_0, strides = var_416, weight = decoder_up_blocks_2_resnets_0_conv1_weight, x = input_131)[name = tensor("input_133")]; + tensor reshape_72_shape_0 = const()[name = tensor("reshape_72_shape_0"), val = tensor([1, 32, 8, 512, 512])]; + tensor reshape_72 = reshape(shape = reshape_72_shape_0, x = input_133)[name = tensor("reshape_72")]; + tensor reduce_mean_54_axes_0 = const()[name = tensor("reduce_mean_54_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_54_keep_dims_0 = const()[name = tensor("reduce_mean_54_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_54 = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72)[name = tensor("reduce_mean_54")]; + tensor sub_36 = sub(x = reshape_72, y = reduce_mean_54)[name = tensor("sub_36")]; + tensor square_18 = square(x = sub_36)[name = tensor("square_18")]; + tensor reduce_mean_56_axes_0 = const()[name = tensor("reduce_mean_56_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_56_keep_dims_0 = const()[name = tensor("reduce_mean_56_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_56 = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18)[name = tensor("reduce_mean_56")]; + tensor add_36_y_0 = const()[name = tensor("add_36_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_36 = add(x = reduce_mean_56, y = add_36_y_0)[name = tensor("add_36")]; + tensor sqrt_18 = sqrt(x = add_36)[name = tensor("sqrt_18")]; + tensor real_div_18 = real_div(x = sub_36, y = sqrt_18)[name = tensor("real_div_18")]; + tensor reshape_73_shape_0 = const()[name = tensor("reshape_73_shape_0"), val = tensor([1, 256, 512, 512])]; + tensor reshape_73 = reshape(shape = reshape_73_shape_0, x = real_div_18)[name = tensor("reshape_73")]; + tensor add_37_mean_0 = const()[name = tensor("add_37_mean_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197953920)))]; + tensor add_37_variance_0 = const()[name = tensor("add_37_variance_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197955008)))]; + tensor add_37_gamma_0 = const()[name = tensor("add_37_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197956096)))]; + tensor add_37_beta_0 = const()[name = tensor("add_37_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197957184)))]; + tensor add_37_epsilon_0 = const()[name = tensor("add_37_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_37 = batch_norm(beta = add_37_beta_0, epsilon = add_37_epsilon_0, gamma = add_37_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_73)[name = tensor("add_37")]; + tensor input_137 = silu(x = add_37)[name = tensor("input_137")]; + tensor var_428 = const()[name = tensor("op_428"), val = tensor([1, 1])]; + tensor var_430 = const()[name = tensor("op_430"), val = tensor([1, 1])]; + tensor hidden_states_35_pad_type_0 = const()[name = tensor("hidden_states_35_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_35_pad_0 = const()[name = tensor("hidden_states_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_35 = conv(bias = decoder_up_blocks_2_resnets_0_conv2_bias, dilations = var_430, groups = var_26, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = var_428, weight = decoder_up_blocks_2_resnets_0_conv2_weight, x = input_137)[name = tensor("hidden_states_35")]; + tensor var_435 = const()[name = tensor("op_435"), val = tensor([1, 1])]; + tensor var_437 = const()[name = tensor("op_437"), val = tensor([1, 1])]; + tensor input_tensor_1_pad_type_0 = const()[name = tensor("input_tensor_1_pad_type_0"), val = tensor("custom")]; + tensor input_tensor_1_pad_0 = const()[name = tensor("input_tensor_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_tensor_1 = conv(bias = decoder_up_blocks_2_resnets_0_conv_shortcut_bias, dilations = var_437, groups = var_26, pad = input_tensor_1_pad_0, pad_type = input_tensor_1_pad_type_0, strides = var_435, weight = decoder_up_blocks_2_resnets_0_conv_shortcut_weight, x = input_127)[name = tensor("input_tensor_1")]; + tensor var_440 = add(x = input_tensor_1, y = hidden_states_35)[name = tensor("op_440")]; + tensor reshape_76_shape_0 = const()[name = tensor("reshape_76_shape_0"), val = tensor([1, 32, 8, 512, 512])]; + tensor reshape_76 = reshape(shape = reshape_76_shape_0, x = var_440)[name = tensor("reshape_76")]; + tensor reduce_mean_57_axes_0 = const()[name = tensor("reduce_mean_57_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_57_keep_dims_0 = const()[name = tensor("reduce_mean_57_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_57 = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76)[name = tensor("reduce_mean_57")]; + tensor sub_38 = sub(x = reshape_76, y = reduce_mean_57)[name = tensor("sub_38")]; + tensor square_19 = square(x = sub_38)[name = tensor("square_19")]; + tensor reduce_mean_59_axes_0 = const()[name = tensor("reduce_mean_59_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_59_keep_dims_0 = const()[name = tensor("reduce_mean_59_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_59 = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19)[name = tensor("reduce_mean_59")]; + tensor add_38_y_0 = const()[name = tensor("add_38_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_38 = add(x = reduce_mean_59, y = add_38_y_0)[name = tensor("add_38")]; + tensor sqrt_19 = sqrt(x = add_38)[name = tensor("sqrt_19")]; + tensor real_div_19 = real_div(x = sub_38, y = sqrt_19)[name = tensor("real_div_19")]; + tensor reshape_77_shape_0 = const()[name = tensor("reshape_77_shape_0"), val = tensor([1, 256, 512, 512])]; + tensor reshape_77 = reshape(shape = reshape_77_shape_0, x = real_div_19)[name = tensor("reshape_77")]; + tensor add_39_gamma_0 = const()[name = tensor("add_39_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197958272)))]; + tensor add_39_beta_0 = const()[name = tensor("add_39_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197959360)))]; + tensor add_39_epsilon_0 = const()[name = tensor("add_39_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_39 = batch_norm(beta = add_39_beta_0, epsilon = add_39_epsilon_0, gamma = add_39_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_77)[name = tensor("add_39")]; + tensor input_145 = silu(x = add_39)[name = tensor("input_145")]; + tensor var_453 = const()[name = tensor("op_453"), val = tensor([1, 1])]; + tensor var_455 = const()[name = tensor("op_455"), val = tensor([1, 1])]; + tensor input_147_pad_type_0 = const()[name = tensor("input_147_pad_type_0"), val = tensor("custom")]; + tensor input_147_pad_0 = const()[name = tensor("input_147_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_147 = conv(bias = decoder_up_blocks_2_resnets_1_conv1_bias, dilations = var_455, groups = var_26, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = var_453, weight = decoder_up_blocks_2_resnets_1_conv1_weight, x = input_145)[name = tensor("input_147")]; + tensor reshape_80_shape_0 = const()[name = tensor("reshape_80_shape_0"), val = tensor([1, 32, 8, 512, 512])]; + tensor reshape_80 = reshape(shape = reshape_80_shape_0, x = input_147)[name = tensor("reshape_80")]; + tensor reduce_mean_60_axes_0 = const()[name = tensor("reduce_mean_60_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_60_keep_dims_0 = const()[name = tensor("reduce_mean_60_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_60 = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80)[name = tensor("reduce_mean_60")]; + tensor sub_40 = sub(x = reshape_80, y = reduce_mean_60)[name = tensor("sub_40")]; + tensor square_20 = square(x = sub_40)[name = tensor("square_20")]; + tensor reduce_mean_62_axes_0 = const()[name = tensor("reduce_mean_62_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_62_keep_dims_0 = const()[name = tensor("reduce_mean_62_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_62 = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20)[name = tensor("reduce_mean_62")]; + tensor add_40_y_0 = const()[name = tensor("add_40_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_40 = add(x = reduce_mean_62, y = add_40_y_0)[name = tensor("add_40")]; + tensor sqrt_20 = sqrt(x = add_40)[name = tensor("sqrt_20")]; + tensor real_div_20 = real_div(x = sub_40, y = sqrt_20)[name = tensor("real_div_20")]; + tensor reshape_81_shape_0 = const()[name = tensor("reshape_81_shape_0"), val = tensor([1, 256, 512, 512])]; + tensor reshape_81 = reshape(shape = reshape_81_shape_0, x = real_div_20)[name = tensor("reshape_81")]; + tensor add_41_gamma_0 = const()[name = tensor("add_41_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197960448)))]; + tensor add_41_beta_0 = const()[name = tensor("add_41_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197961536)))]; + tensor add_41_epsilon_0 = const()[name = tensor("add_41_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_41 = batch_norm(beta = add_41_beta_0, epsilon = add_41_epsilon_0, gamma = add_41_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_81)[name = tensor("add_41")]; + tensor input_151 = silu(x = add_41)[name = tensor("input_151")]; + tensor var_465 = const()[name = tensor("op_465"), val = tensor([1, 1])]; + tensor var_467 = const()[name = tensor("op_467"), val = tensor([1, 1])]; + tensor hidden_states_37_pad_type_0 = const()[name = tensor("hidden_states_37_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_37_pad_0 = const()[name = tensor("hidden_states_37_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_37 = conv(bias = decoder_up_blocks_2_resnets_1_conv2_bias, dilations = var_467, groups = var_26, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = var_465, weight = decoder_up_blocks_2_resnets_1_conv2_weight, x = input_151)[name = tensor("hidden_states_37")]; + tensor var_470 = add(x = var_440, y = hidden_states_37)[name = tensor("op_470")]; + tensor reshape_84_shape_0 = const()[name = tensor("reshape_84_shape_0"), val = tensor([1, 32, 8, 512, 512])]; + tensor reshape_84 = reshape(shape = reshape_84_shape_0, x = var_470)[name = tensor("reshape_84")]; + tensor reduce_mean_63_axes_0 = const()[name = tensor("reduce_mean_63_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_63_keep_dims_0 = const()[name = tensor("reduce_mean_63_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_63 = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84)[name = tensor("reduce_mean_63")]; + tensor sub_42 = sub(x = reshape_84, y = reduce_mean_63)[name = tensor("sub_42")]; + tensor square_21 = square(x = sub_42)[name = tensor("square_21")]; + tensor reduce_mean_65_axes_0 = const()[name = tensor("reduce_mean_65_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_65_keep_dims_0 = const()[name = tensor("reduce_mean_65_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_65 = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21)[name = tensor("reduce_mean_65")]; + tensor add_42_y_0 = const()[name = tensor("add_42_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_42 = add(x = reduce_mean_65, y = add_42_y_0)[name = tensor("add_42")]; + tensor sqrt_21 = sqrt(x = add_42)[name = tensor("sqrt_21")]; + tensor real_div_21 = real_div(x = sub_42, y = sqrt_21)[name = tensor("real_div_21")]; + tensor reshape_85_shape_0 = const()[name = tensor("reshape_85_shape_0"), val = tensor([1, 256, 512, 512])]; + tensor reshape_85 = reshape(shape = reshape_85_shape_0, x = real_div_21)[name = tensor("reshape_85")]; + tensor add_43_gamma_0 = const()[name = tensor("add_43_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197962624)))]; + tensor add_43_beta_0 = const()[name = tensor("add_43_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197963712)))]; + tensor add_43_epsilon_0 = const()[name = tensor("add_43_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_43 = batch_norm(beta = add_43_beta_0, epsilon = add_43_epsilon_0, gamma = add_43_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_85)[name = tensor("add_43")]; + tensor input_159 = silu(x = add_43)[name = tensor("input_159")]; + tensor var_483 = const()[name = tensor("op_483"), val = tensor([1, 1])]; + tensor var_485 = const()[name = tensor("op_485"), val = tensor([1, 1])]; + tensor input_161_pad_type_0 = const()[name = tensor("input_161_pad_type_0"), val = tensor("custom")]; + tensor input_161_pad_0 = const()[name = tensor("input_161_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_161 = conv(bias = decoder_up_blocks_2_resnets_2_conv1_bias, dilations = var_485, groups = var_26, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = var_483, weight = decoder_up_blocks_2_resnets_2_conv1_weight, x = input_159)[name = tensor("input_161")]; + tensor reshape_88_shape_0 = const()[name = tensor("reshape_88_shape_0"), val = tensor([1, 32, 8, 512, 512])]; + tensor reshape_88 = reshape(shape = reshape_88_shape_0, x = input_161)[name = tensor("reshape_88")]; + tensor reduce_mean_66_axes_0 = const()[name = tensor("reduce_mean_66_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_66_keep_dims_0 = const()[name = tensor("reduce_mean_66_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_66 = reduce_mean(axes = reduce_mean_66_axes_0, keep_dims = reduce_mean_66_keep_dims_0, x = reshape_88)[name = tensor("reduce_mean_66")]; + tensor sub_44 = sub(x = reshape_88, y = reduce_mean_66)[name = tensor("sub_44")]; + tensor square_22 = square(x = sub_44)[name = tensor("square_22")]; + tensor reduce_mean_68_axes_0 = const()[name = tensor("reduce_mean_68_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_68_keep_dims_0 = const()[name = tensor("reduce_mean_68_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_68 = reduce_mean(axes = reduce_mean_68_axes_0, keep_dims = reduce_mean_68_keep_dims_0, x = square_22)[name = tensor("reduce_mean_68")]; + tensor add_44_y_0 = const()[name = tensor("add_44_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_44 = add(x = reduce_mean_68, y = add_44_y_0)[name = tensor("add_44")]; + tensor sqrt_22 = sqrt(x = add_44)[name = tensor("sqrt_22")]; + tensor real_div_22 = real_div(x = sub_44, y = sqrt_22)[name = tensor("real_div_22")]; + tensor reshape_89_shape_0 = const()[name = tensor("reshape_89_shape_0"), val = tensor([1, 256, 512, 512])]; + tensor reshape_89 = reshape(shape = reshape_89_shape_0, x = real_div_22)[name = tensor("reshape_89")]; + tensor add_45_gamma_0 = const()[name = tensor("add_45_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197964800)))]; + tensor add_45_beta_0 = const()[name = tensor("add_45_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197965888)))]; + tensor add_45_epsilon_0 = const()[name = tensor("add_45_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_45 = batch_norm(beta = add_45_beta_0, epsilon = add_45_epsilon_0, gamma = add_45_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_89)[name = tensor("add_45")]; + tensor input_165 = silu(x = add_45)[name = tensor("input_165")]; + tensor var_495 = const()[name = tensor("op_495"), val = tensor([1, 1])]; + tensor var_497 = const()[name = tensor("op_497"), val = tensor([1, 1])]; + tensor hidden_states_39_pad_type_0 = const()[name = tensor("hidden_states_39_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_39_pad_0 = const()[name = tensor("hidden_states_39_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_39 = conv(bias = decoder_up_blocks_2_resnets_2_conv2_bias, dilations = var_497, groups = var_26, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = var_495, weight = decoder_up_blocks_2_resnets_2_conv2_weight, x = input_165)[name = tensor("hidden_states_39")]; + tensor var_500 = add(x = var_470, y = hidden_states_39)[name = tensor("op_500")]; + tensor input_169_scale_factor_height_0 = const()[name = tensor("input_169_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_169_scale_factor_width_0 = const()[name = tensor("input_169_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_169 = upsample_nearest_neighbor(scale_factor_height = input_169_scale_factor_height_0, scale_factor_width = input_169_scale_factor_width_0, x = var_500)[name = tensor("input_169")]; + tensor var_508 = const()[name = tensor("op_508"), val = tensor([1, 1])]; + tensor var_510 = const()[name = tensor("op_510"), val = tensor([1, 1])]; + tensor input_171_pad_type_0 = const()[name = tensor("input_171_pad_type_0"), val = tensor("custom")]; + tensor input_171_pad_0 = const()[name = tensor("input_171_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_171 = conv(bias = decoder_up_blocks_2_upsamplers_0_conv_bias, dilations = var_510, groups = var_26, pad = input_171_pad_0, pad_type = input_171_pad_type_0, strides = var_508, weight = decoder_up_blocks_2_upsamplers_0_conv_weight, x = input_169)[name = tensor("input_171")]; + tensor reshape_92_shape_0 = const()[name = tensor("reshape_92_shape_0"), val = tensor([1, 32, 8, 1024, 1024])]; + tensor reshape_92 = reshape(shape = reshape_92_shape_0, x = input_171)[name = tensor("reshape_92")]; + tensor reduce_mean_69_axes_0 = const()[name = tensor("reduce_mean_69_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_69_keep_dims_0 = const()[name = tensor("reduce_mean_69_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_69 = reduce_mean(axes = reduce_mean_69_axes_0, keep_dims = reduce_mean_69_keep_dims_0, x = reshape_92)[name = tensor("reduce_mean_69")]; + tensor sub_46 = sub(x = reshape_92, y = reduce_mean_69)[name = tensor("sub_46")]; + tensor square_23 = square(x = sub_46)[name = tensor("square_23")]; + tensor reduce_mean_71_axes_0 = const()[name = tensor("reduce_mean_71_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_71_keep_dims_0 = const()[name = tensor("reduce_mean_71_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_71 = reduce_mean(axes = reduce_mean_71_axes_0, keep_dims = reduce_mean_71_keep_dims_0, x = square_23)[name = tensor("reduce_mean_71")]; + tensor add_46_y_0 = const()[name = tensor("add_46_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_46 = add(x = reduce_mean_71, y = add_46_y_0)[name = tensor("add_46")]; + tensor sqrt_23 = sqrt(x = add_46)[name = tensor("sqrt_23")]; + tensor real_div_23 = real_div(x = sub_46, y = sqrt_23)[name = tensor("real_div_23")]; + tensor reshape_93_shape_0 = const()[name = tensor("reshape_93_shape_0"), val = tensor([1, 256, 1024, 1024])]; + tensor reshape_93 = reshape(shape = reshape_93_shape_0, x = real_div_23)[name = tensor("reshape_93")]; + tensor add_47_gamma_0 = const()[name = tensor("add_47_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197966976)))]; + tensor add_47_beta_0 = const()[name = tensor("add_47_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197968064)))]; + tensor add_47_epsilon_0 = const()[name = tensor("add_47_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_47 = batch_norm(beta = add_47_beta_0, epsilon = add_47_epsilon_0, gamma = add_47_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_93)[name = tensor("add_47")]; + tensor input_175 = silu(x = add_47)[name = tensor("input_175")]; + tensor var_530 = const()[name = tensor("op_530"), val = tensor([1, 1])]; + tensor var_532 = const()[name = tensor("op_532"), val = tensor([1, 1])]; + tensor input_177_pad_type_0 = const()[name = tensor("input_177_pad_type_0"), val = tensor("custom")]; + tensor input_177_pad_0 = const()[name = tensor("input_177_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_177 = conv(bias = decoder_up_blocks_3_resnets_0_conv1_bias, dilations = var_532, groups = var_26, pad = input_177_pad_0, pad_type = input_177_pad_type_0, strides = var_530, weight = decoder_up_blocks_3_resnets_0_conv1_weight, x = input_175)[name = tensor("input_177")]; + tensor reshape_96_shape_0 = const()[name = tensor("reshape_96_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; + tensor reshape_96 = reshape(shape = reshape_96_shape_0, x = input_177)[name = tensor("reshape_96")]; + tensor reduce_mean_72_axes_0 = const()[name = tensor("reduce_mean_72_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_72_keep_dims_0 = const()[name = tensor("reduce_mean_72_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_72 = reduce_mean(axes = reduce_mean_72_axes_0, keep_dims = reduce_mean_72_keep_dims_0, x = reshape_96)[name = tensor("reduce_mean_72")]; + tensor sub_48 = sub(x = reshape_96, y = reduce_mean_72)[name = tensor("sub_48")]; + tensor square_24 = square(x = sub_48)[name = tensor("square_24")]; + tensor reduce_mean_74_axes_0 = const()[name = tensor("reduce_mean_74_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_74_keep_dims_0 = const()[name = tensor("reduce_mean_74_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_74 = reduce_mean(axes = reduce_mean_74_axes_0, keep_dims = reduce_mean_74_keep_dims_0, x = square_24)[name = tensor("reduce_mean_74")]; + tensor add_48_y_0 = const()[name = tensor("add_48_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_48 = add(x = reduce_mean_74, y = add_48_y_0)[name = tensor("add_48")]; + tensor sqrt_24 = sqrt(x = add_48)[name = tensor("sqrt_24")]; + tensor real_div_24 = real_div(x = sub_48, y = sqrt_24)[name = tensor("real_div_24")]; + tensor reshape_97_shape_0 = const()[name = tensor("reshape_97_shape_0"), val = tensor([1, 128, 1024, 1024])]; + tensor reshape_97 = reshape(shape = reshape_97_shape_0, x = real_div_24)[name = tensor("reshape_97")]; + tensor add_49_mean_0 = const()[name = tensor("add_49_mean_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197969152)))]; + tensor add_49_variance_0 = const()[name = tensor("add_49_variance_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197969728)))]; + tensor add_49_gamma_0 = const()[name = tensor("add_49_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197970304)))]; + tensor add_49_beta_0 = const()[name = tensor("add_49_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197970880)))]; + tensor add_49_epsilon_0 = const()[name = tensor("add_49_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_49 = batch_norm(beta = add_49_beta_0, epsilon = add_49_epsilon_0, gamma = add_49_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_97)[name = tensor("add_49")]; + tensor input_181 = silu(x = add_49)[name = tensor("input_181")]; + tensor var_542 = const()[name = tensor("op_542"), val = tensor([1, 1])]; + tensor var_544 = const()[name = tensor("op_544"), val = tensor([1, 1])]; + tensor hidden_states_43_pad_type_0 = const()[name = tensor("hidden_states_43_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_43_pad_0 = const()[name = tensor("hidden_states_43_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_43 = conv(bias = decoder_up_blocks_3_resnets_0_conv2_bias, dilations = var_544, groups = var_26, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = var_542, weight = decoder_up_blocks_3_resnets_0_conv2_weight, x = input_181)[name = tensor("hidden_states_43")]; + tensor var_549 = const()[name = tensor("op_549"), val = tensor([1, 1])]; + tensor var_551 = const()[name = tensor("op_551"), val = tensor([1, 1])]; + tensor input_tensor_pad_type_0 = const()[name = tensor("input_tensor_pad_type_0"), val = tensor("custom")]; + tensor input_tensor_pad_0 = const()[name = tensor("input_tensor_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_tensor = conv(bias = decoder_up_blocks_3_resnets_0_conv_shortcut_bias, dilations = var_551, groups = var_26, pad = input_tensor_pad_0, pad_type = input_tensor_pad_type_0, strides = var_549, weight = decoder_up_blocks_3_resnets_0_conv_shortcut_weight, x = input_171)[name = tensor("input_tensor")]; + tensor var_554 = add(x = input_tensor, y = hidden_states_43)[name = tensor("op_554")]; + tensor reshape_100_shape_0 = const()[name = tensor("reshape_100_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; + tensor reshape_100 = reshape(shape = reshape_100_shape_0, x = var_554)[name = tensor("reshape_100")]; + tensor reduce_mean_75_axes_0 = const()[name = tensor("reduce_mean_75_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_75_keep_dims_0 = const()[name = tensor("reduce_mean_75_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_75 = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = reshape_100)[name = tensor("reduce_mean_75")]; + tensor sub_50 = sub(x = reshape_100, y = reduce_mean_75)[name = tensor("sub_50")]; + tensor square_25 = square(x = sub_50)[name = tensor("square_25")]; + tensor reduce_mean_77_axes_0 = const()[name = tensor("reduce_mean_77_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_77_keep_dims_0 = const()[name = tensor("reduce_mean_77_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_77 = reduce_mean(axes = reduce_mean_77_axes_0, keep_dims = reduce_mean_77_keep_dims_0, x = square_25)[name = tensor("reduce_mean_77")]; + tensor add_50_y_0 = const()[name = tensor("add_50_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_50 = add(x = reduce_mean_77, y = add_50_y_0)[name = tensor("add_50")]; + tensor sqrt_25 = sqrt(x = add_50)[name = tensor("sqrt_25")]; + tensor real_div_25 = real_div(x = sub_50, y = sqrt_25)[name = tensor("real_div_25")]; + tensor reshape_101_shape_0 = const()[name = tensor("reshape_101_shape_0"), val = tensor([1, 128, 1024, 1024])]; + tensor reshape_101 = reshape(shape = reshape_101_shape_0, x = real_div_25)[name = tensor("reshape_101")]; + tensor add_51_gamma_0 = const()[name = tensor("add_51_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197971456)))]; + tensor add_51_beta_0 = const()[name = tensor("add_51_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197972032)))]; + tensor add_51_epsilon_0 = const()[name = tensor("add_51_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_51 = batch_norm(beta = add_51_beta_0, epsilon = add_51_epsilon_0, gamma = add_51_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_101)[name = tensor("add_51")]; + tensor input_189 = silu(x = add_51)[name = tensor("input_189")]; + tensor var_567 = const()[name = tensor("op_567"), val = tensor([1, 1])]; + tensor var_569 = const()[name = tensor("op_569"), val = tensor([1, 1])]; + tensor input_191_pad_type_0 = const()[name = tensor("input_191_pad_type_0"), val = tensor("custom")]; + tensor input_191_pad_0 = const()[name = tensor("input_191_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_191 = conv(bias = decoder_up_blocks_3_resnets_1_conv1_bias, dilations = var_569, groups = var_26, pad = input_191_pad_0, pad_type = input_191_pad_type_0, strides = var_567, weight = decoder_up_blocks_3_resnets_1_conv1_weight, x = input_189)[name = tensor("input_191")]; + tensor reshape_104_shape_0 = const()[name = tensor("reshape_104_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; + tensor reshape_104 = reshape(shape = reshape_104_shape_0, x = input_191)[name = tensor("reshape_104")]; + tensor reduce_mean_78_axes_0 = const()[name = tensor("reduce_mean_78_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_78_keep_dims_0 = const()[name = tensor("reduce_mean_78_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_78 = reduce_mean(axes = reduce_mean_78_axes_0, keep_dims = reduce_mean_78_keep_dims_0, x = reshape_104)[name = tensor("reduce_mean_78")]; + tensor sub_52 = sub(x = reshape_104, y = reduce_mean_78)[name = tensor("sub_52")]; + tensor square_26 = square(x = sub_52)[name = tensor("square_26")]; + tensor reduce_mean_80_axes_0 = const()[name = tensor("reduce_mean_80_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_80_keep_dims_0 = const()[name = tensor("reduce_mean_80_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_80 = reduce_mean(axes = reduce_mean_80_axes_0, keep_dims = reduce_mean_80_keep_dims_0, x = square_26)[name = tensor("reduce_mean_80")]; + tensor add_52_y_0 = const()[name = tensor("add_52_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_52 = add(x = reduce_mean_80, y = add_52_y_0)[name = tensor("add_52")]; + tensor sqrt_26 = sqrt(x = add_52)[name = tensor("sqrt_26")]; + tensor real_div_26 = real_div(x = sub_52, y = sqrt_26)[name = tensor("real_div_26")]; + tensor reshape_105_shape_0 = const()[name = tensor("reshape_105_shape_0"), val = tensor([1, 128, 1024, 1024])]; + tensor reshape_105 = reshape(shape = reshape_105_shape_0, x = real_div_26)[name = tensor("reshape_105")]; + tensor add_53_gamma_0 = const()[name = tensor("add_53_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197972608)))]; + tensor add_53_beta_0 = const()[name = tensor("add_53_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197973184)))]; + tensor add_53_epsilon_0 = const()[name = tensor("add_53_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_53 = batch_norm(beta = add_53_beta_0, epsilon = add_53_epsilon_0, gamma = add_53_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_105)[name = tensor("add_53")]; + tensor input_195 = silu(x = add_53)[name = tensor("input_195")]; + tensor var_579 = const()[name = tensor("op_579"), val = tensor([1, 1])]; + tensor var_581 = const()[name = tensor("op_581"), val = tensor([1, 1])]; + tensor hidden_states_45_pad_type_0 = const()[name = tensor("hidden_states_45_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_45_pad_0 = const()[name = tensor("hidden_states_45_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_45 = conv(bias = decoder_up_blocks_3_resnets_1_conv2_bias, dilations = var_581, groups = var_26, pad = hidden_states_45_pad_0, pad_type = hidden_states_45_pad_type_0, strides = var_579, weight = decoder_up_blocks_3_resnets_1_conv2_weight, x = input_195)[name = tensor("hidden_states_45")]; + tensor var_584 = add(x = var_554, y = hidden_states_45)[name = tensor("op_584")]; + tensor reshape_108_shape_0 = const()[name = tensor("reshape_108_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; + tensor reshape_108 = reshape(shape = reshape_108_shape_0, x = var_584)[name = tensor("reshape_108")]; + tensor reduce_mean_81_axes_0 = const()[name = tensor("reduce_mean_81_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_81_keep_dims_0 = const()[name = tensor("reduce_mean_81_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_81 = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = reshape_108)[name = tensor("reduce_mean_81")]; + tensor sub_54 = sub(x = reshape_108, y = reduce_mean_81)[name = tensor("sub_54")]; + tensor square_27 = square(x = sub_54)[name = tensor("square_27")]; + tensor reduce_mean_83_axes_0 = const()[name = tensor("reduce_mean_83_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_83_keep_dims_0 = const()[name = tensor("reduce_mean_83_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_83 = reduce_mean(axes = reduce_mean_83_axes_0, keep_dims = reduce_mean_83_keep_dims_0, x = square_27)[name = tensor("reduce_mean_83")]; + tensor add_54_y_0 = const()[name = tensor("add_54_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_54 = add(x = reduce_mean_83, y = add_54_y_0)[name = tensor("add_54")]; + tensor sqrt_27 = sqrt(x = add_54)[name = tensor("sqrt_27")]; + tensor real_div_27 = real_div(x = sub_54, y = sqrt_27)[name = tensor("real_div_27")]; + tensor reshape_109_shape_0 = const()[name = tensor("reshape_109_shape_0"), val = tensor([1, 128, 1024, 1024])]; + tensor reshape_109 = reshape(shape = reshape_109_shape_0, x = real_div_27)[name = tensor("reshape_109")]; + tensor add_55_gamma_0 = const()[name = tensor("add_55_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197973760)))]; + tensor add_55_beta_0 = const()[name = tensor("add_55_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197974336)))]; + tensor add_55_epsilon_0 = const()[name = tensor("add_55_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_55 = batch_norm(beta = add_55_beta_0, epsilon = add_55_epsilon_0, gamma = add_55_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_109)[name = tensor("add_55")]; + tensor input_203 = silu(x = add_55)[name = tensor("input_203")]; + tensor var_597 = const()[name = tensor("op_597"), val = tensor([1, 1])]; + tensor var_599 = const()[name = tensor("op_599"), val = tensor([1, 1])]; + tensor input_205_pad_type_0 = const()[name = tensor("input_205_pad_type_0"), val = tensor("custom")]; + tensor input_205_pad_0 = const()[name = tensor("input_205_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_205 = conv(bias = decoder_up_blocks_3_resnets_2_conv1_bias, dilations = var_599, groups = var_26, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = var_597, weight = decoder_up_blocks_3_resnets_2_conv1_weight, x = input_203)[name = tensor("input_205")]; + tensor reshape_112_shape_0 = const()[name = tensor("reshape_112_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; + tensor reshape_112 = reshape(shape = reshape_112_shape_0, x = input_205)[name = tensor("reshape_112")]; + tensor reduce_mean_84_axes_0 = const()[name = tensor("reduce_mean_84_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_84_keep_dims_0 = const()[name = tensor("reduce_mean_84_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_84 = reduce_mean(axes = reduce_mean_84_axes_0, keep_dims = reduce_mean_84_keep_dims_0, x = reshape_112)[name = tensor("reduce_mean_84")]; + tensor sub_56 = sub(x = reshape_112, y = reduce_mean_84)[name = tensor("sub_56")]; + tensor square_28 = square(x = sub_56)[name = tensor("square_28")]; + tensor reduce_mean_86_axes_0 = const()[name = tensor("reduce_mean_86_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_86_keep_dims_0 = const()[name = tensor("reduce_mean_86_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_86 = reduce_mean(axes = reduce_mean_86_axes_0, keep_dims = reduce_mean_86_keep_dims_0, x = square_28)[name = tensor("reduce_mean_86")]; + tensor add_56_y_0 = const()[name = tensor("add_56_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_56 = add(x = reduce_mean_86, y = add_56_y_0)[name = tensor("add_56")]; + tensor sqrt_28 = sqrt(x = add_56)[name = tensor("sqrt_28")]; + tensor real_div_28 = real_div(x = sub_56, y = sqrt_28)[name = tensor("real_div_28")]; + tensor reshape_113_shape_0 = const()[name = tensor("reshape_113_shape_0"), val = tensor([1, 128, 1024, 1024])]; + tensor reshape_113 = reshape(shape = reshape_113_shape_0, x = real_div_28)[name = tensor("reshape_113")]; + tensor add_57_gamma_0 = const()[name = tensor("add_57_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197974912)))]; + tensor add_57_beta_0 = const()[name = tensor("add_57_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197975488)))]; + tensor add_57_epsilon_0 = const()[name = tensor("add_57_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_57 = batch_norm(beta = add_57_beta_0, epsilon = add_57_epsilon_0, gamma = add_57_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_113)[name = tensor("add_57")]; + tensor input_209 = silu(x = add_57)[name = tensor("input_209")]; + tensor var_609 = const()[name = tensor("op_609"), val = tensor([1, 1])]; + tensor var_611 = const()[name = tensor("op_611"), 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([1, 1, 1, 1])]; + tensor hidden_states = conv(bias = decoder_up_blocks_3_resnets_2_conv2_bias, dilations = var_611, groups = var_26, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_609, weight = decoder_up_blocks_3_resnets_2_conv2_weight, x = input_209)[name = tensor("hidden_states")]; + tensor var_614 = add(x = var_584, y = hidden_states)[name = tensor("op_614")]; + tensor reshape_116_shape_0 = const()[name = tensor("reshape_116_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; + tensor reshape_116 = reshape(shape = reshape_116_shape_0, x = var_614)[name = tensor("reshape_116")]; + tensor reduce_mean_87_axes_0 = const()[name = tensor("reduce_mean_87_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_87_keep_dims_0 = const()[name = tensor("reduce_mean_87_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_87 = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = reshape_116)[name = tensor("reduce_mean_87")]; + tensor sub_58 = sub(x = reshape_116, y = reduce_mean_87)[name = tensor("sub_58")]; + tensor square_29 = square(x = sub_58)[name = tensor("square_29")]; + tensor reduce_mean_89_axes_0 = const()[name = tensor("reduce_mean_89_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_89_keep_dims_0 = const()[name = tensor("reduce_mean_89_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_89 = reduce_mean(axes = reduce_mean_89_axes_0, keep_dims = reduce_mean_89_keep_dims_0, x = square_29)[name = tensor("reduce_mean_89")]; + tensor add_58_y_0 = const()[name = tensor("add_58_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_58 = add(x = reduce_mean_89, y = add_58_y_0)[name = tensor("add_58")]; + tensor sqrt_29 = sqrt(x = add_58)[name = tensor("sqrt_29")]; + tensor real_div_29 = real_div(x = sub_58, y = sqrt_29)[name = tensor("real_div_29")]; + tensor reshape_117_shape_0 = const()[name = tensor("reshape_117_shape_0"), val = tensor([1, 128, 1024, 1024])]; + tensor reshape_117 = reshape(shape = reshape_117_shape_0, x = real_div_29)[name = tensor("reshape_117")]; + tensor add_59_gamma_0 = const()[name = tensor("add_59_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197976064)))]; + tensor add_59_beta_0 = const()[name = tensor("add_59_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197976640)))]; + tensor add_59_epsilon_0 = const()[name = tensor("add_59_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_59 = batch_norm(beta = add_59_beta_0, epsilon = add_59_epsilon_0, gamma = add_59_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_117)[name = tensor("add_59")]; + tensor input = silu(x = add_59)[name = tensor("input")]; + tensor var_623 = const()[name = tensor("op_623"), val = tensor([1, 1])]; + tensor var_625 = const()[name = tensor("op_625"), val = tensor([1, 1])]; + tensor var_627_pad_type_0 = const()[name = tensor("op_627_pad_type_0"), val = tensor("custom")]; + tensor var_627_pad_0 = const()[name = tensor("op_627_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor image = conv(bias = decoder_conv_out_bias, dilations = var_625, groups = var_26, pad = var_627_pad_0, pad_type = var_627_pad_type_0, strides = var_623, weight = decoder_conv_out_weight, x = input)[name = tensor("op_627")]; + } -> (image); +} \ No newline at end of file