diff --git "a/original/compiled/VAEEncoder.mlmodelc/model.mil" "b/original/compiled/VAEEncoder.mlmodelc/model.mil" new file mode 100644--- /dev/null +++ "b/original/compiled/VAEEncoder.mlmodelc/model.mil" @@ -0,0 +1,752 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "5.30.0"}, {"coremlc-version", "1839.0.0"}, {"coremltools-component-torch", "2.0.1"}, {"coremltools-version", "7.0b1"}})] +{ + func main(tensor z) { + tensor var_7 = const()[name = tensor("op_7"), val = tensor(-1)]; + tensor var_17 = const()[name = tensor("op_17"), val = tensor(1)]; + tensor var_35 = const()[name = tensor("op_35"), val = tensor([1, 1])]; + tensor var_37 = const()[name = tensor("op_37"), 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([1, 1, 1, 1])]; + tensor encoder_conv_in_weight_to_fp16 = const()[name = tensor("encoder_conv_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor encoder_conv_in_bias_to_fp16 = const()[name = tensor("encoder_conv_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7040)))]; + tensor input_1_cast = conv(bias = encoder_conv_in_bias_to_fp16, dilations = var_37, groups = var_17, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = var_35, weight = encoder_conv_in_weight_to_fp16, x = z)[name = tensor("input_1_cast")]; + tensor reshape_0_shape_0 = const()[name = tensor("reshape_0_shape_0"), val = tensor([1, 32, 4, 512, 512])]; + tensor reshape_0_cast = reshape(shape = reshape_0_shape_0, x = input_1_cast)[name = tensor("reshape_0_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0_cast)[name = tensor("reduce_mean_0_cast")]; + tensor sub_0_cast = sub(x = reshape_0_cast, y = reduce_mean_0_cast)[name = tensor("sub_0_cast")]; + tensor square_0_cast = square(x = sub_0_cast)[name = tensor("square_0_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0_cast)[name = tensor("reduce_mean_2_cast")]; + tensor add_0_y_0_to_fp16 = const()[name = tensor("add_0_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_0_cast = add(x = reduce_mean_2_cast, y = add_0_y_0_to_fp16)[name = tensor("add_0_cast")]; + tensor sqrt_0_cast = sqrt(x = add_0_cast)[name = tensor("sqrt_0_cast")]; + tensor real_div_0_cast = real_div(x = sub_0_cast, y = sqrt_0_cast)[name = tensor("real_div_0_cast")]; + tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([1, 128, 512, 512])]; + tensor reshape_1_cast = reshape(shape = reshape_1_shape_0, x = real_div_0_cast)[name = tensor("reshape_1_cast")]; + tensor add_1_mean_0_to_fp16 = const()[name = tensor("add_1_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7360)))]; + tensor add_1_variance_0_to_fp16 = const()[name = tensor("add_1_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7680)))]; + tensor add_1_gamma_0_to_fp16 = const()[name = tensor("add_1_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8000)))]; + tensor add_1_beta_0_to_fp16 = const()[name = tensor("add_1_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8320)))]; + tensor add_1_epsilon_0_to_fp16 = const()[name = tensor("add_1_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_1_cast = batch_norm(beta = add_1_beta_0_to_fp16, epsilon = add_1_epsilon_0_to_fp16, gamma = add_1_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_1_cast)[name = tensor("add_1_cast")]; + tensor input_5_cast = silu(x = add_1_cast)[name = tensor("input_5_cast")]; + tensor var_56 = const()[name = tensor("op_56"), val = tensor([1, 1])]; + tensor var_58 = const()[name = tensor("op_58"), val = tensor([1, 1])]; + tensor input_7_pad_type_0 = const()[name = tensor("input_7_pad_type_0"), val = tensor("custom")]; + tensor input_7_pad_0 = const()[name = tensor("input_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_0_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_0_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8640)))]; + tensor encoder_down_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303616)))]; + tensor input_7_cast = conv(bias = encoder_down_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_58, groups = var_17, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = var_56, weight = encoder_down_blocks_0_resnets_0_conv1_weight_to_fp16, x = input_5_cast)[name = tensor("input_7_cast")]; + tensor reshape_4_shape_0 = const()[name = tensor("reshape_4_shape_0"), val = tensor([1, 32, 4, 512, 512])]; + tensor reshape_4_cast = reshape(shape = reshape_4_shape_0, x = input_7_cast)[name = tensor("reshape_4_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4_cast)[name = tensor("reduce_mean_3_cast")]; + tensor sub_2_cast = sub(x = reshape_4_cast, y = reduce_mean_3_cast)[name = tensor("sub_2_cast")]; + tensor square_1_cast = square(x = sub_2_cast)[name = tensor("square_1_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1_cast)[name = tensor("reduce_mean_5_cast")]; + tensor add_2_y_0_to_fp16 = const()[name = tensor("add_2_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_2_cast = add(x = reduce_mean_5_cast, y = add_2_y_0_to_fp16)[name = tensor("add_2_cast")]; + tensor sqrt_1_cast = sqrt(x = add_2_cast)[name = tensor("sqrt_1_cast")]; + tensor real_div_1_cast = real_div(x = sub_2_cast, y = sqrt_1_cast)[name = tensor("real_div_1_cast")]; + tensor reshape_5_shape_0 = const()[name = tensor("reshape_5_shape_0"), val = tensor([1, 128, 512, 512])]; + tensor reshape_5_cast = reshape(shape = reshape_5_shape_0, x = real_div_1_cast)[name = tensor("reshape_5_cast")]; + tensor add_3_gamma_0_to_fp16 = const()[name = tensor("add_3_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(303936)))]; + tensor add_3_beta_0_to_fp16 = const()[name = tensor("add_3_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304256)))]; + tensor add_3_epsilon_0_to_fp16 = const()[name = tensor("add_3_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_3_cast = batch_norm(beta = add_3_beta_0_to_fp16, epsilon = add_3_epsilon_0_to_fp16, gamma = add_3_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_5_cast)[name = tensor("add_3_cast")]; + tensor input_11_cast = silu(x = add_3_cast)[name = tensor("input_11_cast")]; + tensor var_68 = const()[name = tensor("op_68"), val = tensor([1, 1])]; + tensor var_70 = const()[name = tensor("op_70"), 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 encoder_down_blocks_0_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_0_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(304576)))]; + tensor encoder_down_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(599552)))]; + tensor hidden_states_1_cast = conv(bias = encoder_down_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_70, groups = var_17, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_68, weight = encoder_down_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_11_cast)[name = tensor("hidden_states_1_cast")]; + tensor var_73_cast = add(x = input_1_cast, y = hidden_states_1_cast)[name = tensor("op_73_cast")]; + tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([1, 32, 4, 512, 512])]; + tensor reshape_8_cast = reshape(shape = reshape_8_shape_0, x = var_73_cast)[name = tensor("reshape_8_cast")]; + tensor reduce_mean_6_axes_0 = const()[name = tensor("reduce_mean_6_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_6_keep_dims_0 = const()[name = tensor("reduce_mean_6_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_6_cast = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8_cast)[name = tensor("reduce_mean_6_cast")]; + tensor sub_4_cast = sub(x = reshape_8_cast, y = reduce_mean_6_cast)[name = tensor("sub_4_cast")]; + tensor square_2_cast = square(x = sub_4_cast)[name = tensor("square_2_cast")]; + tensor reduce_mean_8_axes_0 = const()[name = tensor("reduce_mean_8_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_8_keep_dims_0 = const()[name = tensor("reduce_mean_8_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_8_cast = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2_cast)[name = tensor("reduce_mean_8_cast")]; + tensor add_4_y_0_to_fp16 = const()[name = tensor("add_4_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_4_cast = add(x = reduce_mean_8_cast, y = add_4_y_0_to_fp16)[name = tensor("add_4_cast")]; + tensor sqrt_2_cast = sqrt(x = add_4_cast)[name = tensor("sqrt_2_cast")]; + tensor real_div_2_cast = real_div(x = sub_4_cast, y = sqrt_2_cast)[name = tensor("real_div_2_cast")]; + tensor reshape_9_shape_0 = const()[name = tensor("reshape_9_shape_0"), val = tensor([1, 128, 512, 512])]; + tensor reshape_9_cast = reshape(shape = reshape_9_shape_0, x = real_div_2_cast)[name = tensor("reshape_9_cast")]; + tensor add_5_gamma_0_to_fp16 = const()[name = tensor("add_5_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(599872)))]; + tensor add_5_beta_0_to_fp16 = const()[name = tensor("add_5_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(600192)))]; + tensor add_5_epsilon_0_to_fp16 = const()[name = tensor("add_5_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_5_cast = batch_norm(beta = add_5_beta_0_to_fp16, epsilon = add_5_epsilon_0_to_fp16, gamma = add_5_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_9_cast)[name = tensor("add_5_cast")]; + tensor input_19_cast = silu(x = add_5_cast)[name = tensor("input_19_cast")]; + tensor var_86 = const()[name = tensor("op_86"), val = tensor([1, 1])]; + tensor var_88 = const()[name = tensor("op_88"), val = tensor([1, 1])]; + tensor input_21_pad_type_0 = const()[name = tensor("input_21_pad_type_0"), val = tensor("custom")]; + tensor input_21_pad_0 = const()[name = tensor("input_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_0_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_0_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(600512)))]; + tensor encoder_down_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(895488)))]; + tensor input_21_cast = conv(bias = encoder_down_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_88, groups = var_17, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = var_86, weight = encoder_down_blocks_0_resnets_1_conv1_weight_to_fp16, x = input_19_cast)[name = tensor("input_21_cast")]; + tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([1, 32, 4, 512, 512])]; + tensor reshape_12_cast = reshape(shape = reshape_12_shape_0, x = input_21_cast)[name = tensor("reshape_12_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12_cast)[name = tensor("reduce_mean_9_cast")]; + tensor sub_6_cast = sub(x = reshape_12_cast, y = reduce_mean_9_cast)[name = tensor("sub_6_cast")]; + tensor square_3_cast = square(x = sub_6_cast)[name = tensor("square_3_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3_cast)[name = tensor("reduce_mean_11_cast")]; + tensor add_6_y_0_to_fp16 = const()[name = tensor("add_6_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_6_cast = add(x = reduce_mean_11_cast, y = add_6_y_0_to_fp16)[name = tensor("add_6_cast")]; + tensor sqrt_3_cast = sqrt(x = add_6_cast)[name = tensor("sqrt_3_cast")]; + tensor real_div_3_cast = real_div(x = sub_6_cast, y = sqrt_3_cast)[name = tensor("real_div_3_cast")]; + tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([1, 128, 512, 512])]; + tensor reshape_13_cast = reshape(shape = reshape_13_shape_0, x = real_div_3_cast)[name = tensor("reshape_13_cast")]; + tensor add_7_gamma_0_to_fp16 = const()[name = tensor("add_7_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(895808)))]; + tensor add_7_beta_0_to_fp16 = const()[name = tensor("add_7_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(896128)))]; + tensor add_7_epsilon_0_to_fp16 = const()[name = tensor("add_7_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_7_cast = batch_norm(beta = add_7_beta_0_to_fp16, epsilon = add_7_epsilon_0_to_fp16, gamma = add_7_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_13_cast)[name = tensor("add_7_cast")]; + tensor input_25_cast = silu(x = add_7_cast)[name = tensor("input_25_cast")]; + tensor var_98 = const()[name = tensor("op_98"), val = tensor([1, 1])]; + tensor var_100 = const()[name = tensor("op_100"), val = tensor([1, 1])]; + tensor hidden_states_3_pad_type_0 = const()[name = tensor("hidden_states_3_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_3_pad_0 = const()[name = tensor("hidden_states_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_0_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_0_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(896448)))]; + tensor encoder_down_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1191424)))]; + tensor hidden_states_3_cast = conv(bias = encoder_down_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_100, groups = var_17, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = var_98, weight = encoder_down_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_25_cast)[name = tensor("hidden_states_3_cast")]; + tensor var_103_cast = add(x = var_73_cast, y = hidden_states_3_cast)[name = tensor("op_103_cast")]; + tensor hidden_states_7_pad_0 = const()[name = tensor("hidden_states_7_pad_0"), val = tensor([0, 0, 0, 0, 0, 1, 0, 1])]; + tensor hidden_states_7_mode_0 = const()[name = tensor("hidden_states_7_mode_0"), val = tensor("constant")]; + tensor hidden_states_7_constant_val_0_to_fp16 = const()[name = tensor("hidden_states_7_constant_val_0_to_fp16"), val = tensor(0x0p+0)]; + tensor hidden_states_7_cast = pad(constant_val = hidden_states_7_constant_val_0_to_fp16, mode = hidden_states_7_mode_0, pad = hidden_states_7_pad_0, x = var_103_cast)[name = tensor("hidden_states_7_cast")]; + tensor var_111 = const()[name = tensor("op_111"), val = tensor([2, 2])]; + tensor var_113 = const()[name = tensor("op_113"), val = tensor([1, 1])]; + tensor input_29_pad_type_0 = const()[name = tensor("input_29_pad_type_0"), val = tensor("custom")]; + tensor input_29_pad_0 = const()[name = tensor("input_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor encoder_down_blocks_0_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_0_downsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1191744)))]; + tensor encoder_down_blocks_0_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_0_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1486720)))]; + tensor input_29_cast = conv(bias = encoder_down_blocks_0_downsamplers_0_conv_bias_to_fp16, dilations = var_113, groups = var_17, pad = input_29_pad_0, pad_type = input_29_pad_type_0, strides = var_111, weight = encoder_down_blocks_0_downsamplers_0_conv_weight_to_fp16, x = hidden_states_7_cast)[name = tensor("input_29_cast")]; + tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([1, 32, 4, 256, 256])]; + tensor reshape_16_cast = reshape(shape = reshape_16_shape_0, x = input_29_cast)[name = tensor("reshape_16_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16_cast)[name = tensor("reduce_mean_12_cast")]; + tensor sub_8_cast = sub(x = reshape_16_cast, y = reduce_mean_12_cast)[name = tensor("sub_8_cast")]; + tensor square_4_cast = square(x = sub_8_cast)[name = tensor("square_4_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4_cast)[name = tensor("reduce_mean_14_cast")]; + tensor add_8_y_0_to_fp16 = const()[name = tensor("add_8_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_8_cast = add(x = reduce_mean_14_cast, y = add_8_y_0_to_fp16)[name = tensor("add_8_cast")]; + tensor sqrt_4_cast = sqrt(x = add_8_cast)[name = tensor("sqrt_4_cast")]; + tensor real_div_4_cast = real_div(x = sub_8_cast, y = sqrt_4_cast)[name = tensor("real_div_4_cast")]; + tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([1, 128, 256, 256])]; + tensor reshape_17_cast = reshape(shape = reshape_17_shape_0, x = real_div_4_cast)[name = tensor("reshape_17_cast")]; + tensor add_9_gamma_0_to_fp16 = const()[name = tensor("add_9_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1487040)))]; + tensor add_9_beta_0_to_fp16 = const()[name = tensor("add_9_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1487360)))]; + tensor add_9_epsilon_0_to_fp16 = const()[name = tensor("add_9_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_9_cast = batch_norm(beta = add_9_beta_0_to_fp16, epsilon = add_9_epsilon_0_to_fp16, gamma = add_9_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_17_cast)[name = tensor("add_9_cast")]; + tensor input_33_cast = silu(x = add_9_cast)[name = tensor("input_33_cast")]; + tensor var_133 = const()[name = tensor("op_133"), val = tensor([1, 1])]; + tensor var_135 = const()[name = tensor("op_135"), val = tensor([1, 1])]; + tensor input_35_pad_type_0 = const()[name = tensor("input_35_pad_type_0"), val = tensor("custom")]; + tensor input_35_pad_0 = const()[name = tensor("input_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_1_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1487680)))]; + tensor encoder_down_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2077568)))]; + tensor input_35_cast = conv(bias = encoder_down_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_135, groups = var_17, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = var_133, weight = encoder_down_blocks_1_resnets_0_conv1_weight_to_fp16, x = input_33_cast)[name = tensor("input_35_cast")]; + tensor reshape_20_shape_0 = const()[name = tensor("reshape_20_shape_0"), val = tensor([1, 32, 8, 256, 256])]; + tensor reshape_20_cast = reshape(shape = reshape_20_shape_0, x = input_35_cast)[name = tensor("reshape_20_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20_cast)[name = tensor("reduce_mean_15_cast")]; + tensor sub_10_cast = sub(x = reshape_20_cast, y = reduce_mean_15_cast)[name = tensor("sub_10_cast")]; + tensor square_5_cast = square(x = sub_10_cast)[name = tensor("square_5_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5_cast)[name = tensor("reduce_mean_17_cast")]; + tensor add_10_y_0_to_fp16 = const()[name = tensor("add_10_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_10_cast = add(x = reduce_mean_17_cast, y = add_10_y_0_to_fp16)[name = tensor("add_10_cast")]; + tensor sqrt_5_cast = sqrt(x = add_10_cast)[name = tensor("sqrt_5_cast")]; + tensor real_div_5_cast = real_div(x = sub_10_cast, y = sqrt_5_cast)[name = tensor("real_div_5_cast")]; + tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([1, 256, 256, 256])]; + tensor reshape_21_cast = reshape(shape = reshape_21_shape_0, x = real_div_5_cast)[name = tensor("reshape_21_cast")]; + tensor add_11_mean_0_to_fp16 = const()[name = tensor("add_11_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2078144)))]; + tensor add_11_variance_0_to_fp16 = const()[name = tensor("add_11_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2078720)))]; + tensor add_11_gamma_0_to_fp16 = const()[name = tensor("add_11_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2079296)))]; + tensor add_11_beta_0_to_fp16 = const()[name = tensor("add_11_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2079872)))]; + tensor add_11_epsilon_0_to_fp16 = const()[name = tensor("add_11_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_11_cast = batch_norm(beta = add_11_beta_0_to_fp16, epsilon = add_11_epsilon_0_to_fp16, gamma = add_11_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_21_cast)[name = tensor("add_11_cast")]; + tensor input_39_cast = silu(x = add_11_cast)[name = tensor("input_39_cast")]; + tensor var_145 = const()[name = tensor("op_145"), val = tensor([1, 1])]; + tensor var_147 = const()[name = tensor("op_147"), val = tensor([1, 1])]; + tensor hidden_states_9_pad_type_0 = const()[name = tensor("hidden_states_9_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_9_pad_0 = const()[name = tensor("hidden_states_9_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_1_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2080448)))]; + tensor encoder_down_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3260160)))]; + tensor hidden_states_9_cast = conv(bias = encoder_down_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_147, groups = var_17, pad = hidden_states_9_pad_0, pad_type = hidden_states_9_pad_type_0, strides = var_145, weight = encoder_down_blocks_1_resnets_0_conv2_weight_to_fp16, x = input_39_cast)[name = tensor("hidden_states_9_cast")]; + tensor var_152 = const()[name = tensor("op_152"), val = tensor([1, 1])]; + tensor var_154 = const()[name = tensor("op_154"), 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 encoder_down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3260736)))]; + tensor encoder_down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3326336)))]; + tensor input_tensor_1_cast = conv(bias = encoder_down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_154, groups = var_17, pad = input_tensor_1_pad_0, pad_type = input_tensor_1_pad_type_0, strides = var_152, weight = encoder_down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16, x = input_29_cast)[name = tensor("input_tensor_1_cast")]; + tensor var_157_cast = add(x = input_tensor_1_cast, y = hidden_states_9_cast)[name = tensor("op_157_cast")]; + tensor reshape_24_shape_0 = const()[name = tensor("reshape_24_shape_0"), val = tensor([1, 32, 8, 256, 256])]; + tensor reshape_24_cast = reshape(shape = reshape_24_shape_0, x = var_157_cast)[name = tensor("reshape_24_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24_cast)[name = tensor("reduce_mean_18_cast")]; + tensor sub_12_cast = sub(x = reshape_24_cast, y = reduce_mean_18_cast)[name = tensor("sub_12_cast")]; + tensor square_6_cast = square(x = sub_12_cast)[name = tensor("square_6_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6_cast)[name = tensor("reduce_mean_20_cast")]; + tensor add_12_y_0_to_fp16 = const()[name = tensor("add_12_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_12_cast = add(x = reduce_mean_20_cast, y = add_12_y_0_to_fp16)[name = tensor("add_12_cast")]; + tensor sqrt_6_cast = sqrt(x = add_12_cast)[name = tensor("sqrt_6_cast")]; + tensor real_div_6_cast = real_div(x = sub_12_cast, y = sqrt_6_cast)[name = tensor("real_div_6_cast")]; + tensor reshape_25_shape_0 = const()[name = tensor("reshape_25_shape_0"), val = tensor([1, 256, 256, 256])]; + tensor reshape_25_cast = reshape(shape = reshape_25_shape_0, x = real_div_6_cast)[name = tensor("reshape_25_cast")]; + tensor add_13_gamma_0_to_fp16 = const()[name = tensor("add_13_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3326912)))]; + tensor add_13_beta_0_to_fp16 = const()[name = tensor("add_13_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3327488)))]; + tensor add_13_epsilon_0_to_fp16 = const()[name = tensor("add_13_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_13_cast = batch_norm(beta = add_13_beta_0_to_fp16, epsilon = add_13_epsilon_0_to_fp16, gamma = add_13_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_25_cast)[name = tensor("add_13_cast")]; + tensor input_47_cast = silu(x = add_13_cast)[name = tensor("input_47_cast")]; + tensor var_170 = const()[name = tensor("op_170"), val = tensor([1, 1])]; + tensor var_172 = const()[name = tensor("op_172"), val = tensor([1, 1])]; + tensor input_49_pad_type_0 = const()[name = tensor("input_49_pad_type_0"), val = tensor("custom")]; + tensor input_49_pad_0 = const()[name = tensor("input_49_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_1_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3328064)))]; + tensor encoder_down_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4507776)))]; + tensor input_49_cast = conv(bias = encoder_down_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_172, groups = var_17, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = var_170, weight = encoder_down_blocks_1_resnets_1_conv1_weight_to_fp16, x = input_47_cast)[name = tensor("input_49_cast")]; + tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([1, 32, 8, 256, 256])]; + tensor reshape_28_cast = reshape(shape = reshape_28_shape_0, x = input_49_cast)[name = tensor("reshape_28_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28_cast)[name = tensor("reduce_mean_21_cast")]; + tensor sub_14_cast = sub(x = reshape_28_cast, y = reduce_mean_21_cast)[name = tensor("sub_14_cast")]; + tensor square_7_cast = square(x = sub_14_cast)[name = tensor("square_7_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7_cast)[name = tensor("reduce_mean_23_cast")]; + tensor add_14_y_0_to_fp16 = const()[name = tensor("add_14_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_14_cast = add(x = reduce_mean_23_cast, y = add_14_y_0_to_fp16)[name = tensor("add_14_cast")]; + tensor sqrt_7_cast = sqrt(x = add_14_cast)[name = tensor("sqrt_7_cast")]; + tensor real_div_7_cast = real_div(x = sub_14_cast, y = sqrt_7_cast)[name = tensor("real_div_7_cast")]; + tensor reshape_29_shape_0 = const()[name = tensor("reshape_29_shape_0"), val = tensor([1, 256, 256, 256])]; + tensor reshape_29_cast = reshape(shape = reshape_29_shape_0, x = real_div_7_cast)[name = tensor("reshape_29_cast")]; + tensor add_15_gamma_0_to_fp16 = const()[name = tensor("add_15_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4508352)))]; + tensor add_15_beta_0_to_fp16 = const()[name = tensor("add_15_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4508928)))]; + tensor add_15_epsilon_0_to_fp16 = const()[name = tensor("add_15_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_15_cast = batch_norm(beta = add_15_beta_0_to_fp16, epsilon = add_15_epsilon_0_to_fp16, gamma = add_15_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_29_cast)[name = tensor("add_15_cast")]; + tensor input_53_cast = silu(x = add_15_cast)[name = tensor("input_53_cast")]; + tensor var_182 = const()[name = tensor("op_182"), val = tensor([1, 1])]; + tensor var_184 = const()[name = tensor("op_184"), val = tensor([1, 1])]; + tensor hidden_states_11_pad_type_0 = const()[name = tensor("hidden_states_11_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_11_pad_0 = const()[name = tensor("hidden_states_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_1_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4509504)))]; + tensor encoder_down_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5689216)))]; + tensor hidden_states_11_cast = conv(bias = encoder_down_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_184, groups = var_17, pad = hidden_states_11_pad_0, pad_type = hidden_states_11_pad_type_0, strides = var_182, weight = encoder_down_blocks_1_resnets_1_conv2_weight_to_fp16, x = input_53_cast)[name = tensor("hidden_states_11_cast")]; + tensor var_187_cast = add(x = var_157_cast, y = hidden_states_11_cast)[name = tensor("op_187_cast")]; + tensor hidden_states_15_pad_0 = const()[name = tensor("hidden_states_15_pad_0"), val = tensor([0, 0, 0, 0, 0, 1, 0, 1])]; + tensor hidden_states_15_mode_0 = const()[name = tensor("hidden_states_15_mode_0"), val = tensor("constant")]; + tensor hidden_states_15_constant_val_0_to_fp16 = const()[name = tensor("hidden_states_15_constant_val_0_to_fp16"), val = tensor(0x0p+0)]; + tensor hidden_states_15_cast = pad(constant_val = hidden_states_15_constant_val_0_to_fp16, mode = hidden_states_15_mode_0, pad = hidden_states_15_pad_0, x = var_187_cast)[name = tensor("hidden_states_15_cast")]; + tensor var_195 = const()[name = tensor("op_195"), val = tensor([2, 2])]; + tensor var_197 = const()[name = tensor("op_197"), val = tensor([1, 1])]; + tensor input_57_pad_type_0 = const()[name = tensor("input_57_pad_type_0"), val = tensor("custom")]; + tensor input_57_pad_0 = const()[name = tensor("input_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor encoder_down_blocks_1_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_1_downsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5689792)))]; + tensor encoder_down_blocks_1_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_1_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6869504)))]; + tensor input_57_cast = conv(bias = encoder_down_blocks_1_downsamplers_0_conv_bias_to_fp16, dilations = var_197, groups = var_17, pad = input_57_pad_0, pad_type = input_57_pad_type_0, strides = var_195, weight = encoder_down_blocks_1_downsamplers_0_conv_weight_to_fp16, x = hidden_states_15_cast)[name = tensor("input_57_cast")]; + tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([1, 32, 8, 128, 128])]; + tensor reshape_32_cast = reshape(shape = reshape_32_shape_0, x = input_57_cast)[name = tensor("reshape_32_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32_cast)[name = tensor("reduce_mean_24_cast")]; + tensor sub_16_cast = sub(x = reshape_32_cast, y = reduce_mean_24_cast)[name = tensor("sub_16_cast")]; + tensor square_8_cast = square(x = sub_16_cast)[name = tensor("square_8_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8_cast)[name = tensor("reduce_mean_26_cast")]; + tensor add_16_y_0_to_fp16 = const()[name = tensor("add_16_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_16_cast = add(x = reduce_mean_26_cast, y = add_16_y_0_to_fp16)[name = tensor("add_16_cast")]; + tensor sqrt_8_cast = sqrt(x = add_16_cast)[name = tensor("sqrt_8_cast")]; + tensor real_div_8_cast = real_div(x = sub_16_cast, y = sqrt_8_cast)[name = tensor("real_div_8_cast")]; + tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([1, 256, 128, 128])]; + tensor reshape_33_cast = reshape(shape = reshape_33_shape_0, x = real_div_8_cast)[name = tensor("reshape_33_cast")]; + tensor add_17_gamma_0_to_fp16 = const()[name = tensor("add_17_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6870080)))]; + tensor add_17_beta_0_to_fp16 = const()[name = tensor("add_17_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6870656)))]; + tensor add_17_epsilon_0_to_fp16 = const()[name = tensor("add_17_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_17_cast = batch_norm(beta = add_17_beta_0_to_fp16, epsilon = add_17_epsilon_0_to_fp16, gamma = add_17_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_33_cast)[name = tensor("add_17_cast")]; + tensor input_61_cast = silu(x = add_17_cast)[name = tensor("input_61_cast")]; + tensor var_217 = const()[name = tensor("op_217"), val = tensor([1, 1])]; + tensor var_219 = const()[name = tensor("op_219"), val = tensor([1, 1])]; + tensor input_63_pad_type_0 = const()[name = tensor("input_63_pad_type_0"), val = tensor("custom")]; + tensor input_63_pad_0 = const()[name = tensor("input_63_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_2_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6871232)))]; + tensor encoder_down_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9230592)))]; + tensor input_63_cast = conv(bias = encoder_down_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_219, groups = var_17, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = var_217, weight = encoder_down_blocks_2_resnets_0_conv1_weight_to_fp16, x = input_61_cast)[name = tensor("input_63_cast")]; + tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_36_cast = reshape(shape = reshape_36_shape_0, x = input_63_cast)[name = tensor("reshape_36_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36_cast)[name = tensor("reduce_mean_27_cast")]; + tensor sub_18_cast = sub(x = reshape_36_cast, y = reduce_mean_27_cast)[name = tensor("sub_18_cast")]; + tensor square_9_cast = square(x = sub_18_cast)[name = tensor("square_9_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9_cast)[name = tensor("reduce_mean_29_cast")]; + tensor add_18_y_0_to_fp16 = const()[name = tensor("add_18_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_18_cast = add(x = reduce_mean_29_cast, y = add_18_y_0_to_fp16)[name = tensor("add_18_cast")]; + tensor sqrt_9_cast = sqrt(x = add_18_cast)[name = tensor("sqrt_9_cast")]; + tensor real_div_9_cast = real_div(x = sub_18_cast, y = sqrt_9_cast)[name = tensor("real_div_9_cast")]; + tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_37_cast = reshape(shape = reshape_37_shape_0, x = real_div_9_cast)[name = tensor("reshape_37_cast")]; + tensor add_19_mean_0_to_fp16 = const()[name = tensor("add_19_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9231680)))]; + tensor add_19_variance_0_to_fp16 = const()[name = tensor("add_19_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9232768)))]; + tensor add_19_gamma_0_to_fp16 = const()[name = tensor("add_19_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9233856)))]; + tensor add_19_beta_0_to_fp16 = const()[name = tensor("add_19_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9234944)))]; + tensor add_19_epsilon_0_to_fp16 = const()[name = tensor("add_19_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_19_cast = batch_norm(beta = add_19_beta_0_to_fp16, epsilon = add_19_epsilon_0_to_fp16, gamma = add_19_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_37_cast)[name = tensor("add_19_cast")]; + tensor input_67_cast = silu(x = add_19_cast)[name = tensor("input_67_cast")]; + 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 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 encoder_down_blocks_2_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9236032)))]; + tensor encoder_down_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13954688)))]; + tensor hidden_states_17_cast = conv(bias = encoder_down_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_231, groups = var_17, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = var_229, weight = encoder_down_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_67_cast)[name = tensor("hidden_states_17_cast")]; + tensor var_236 = const()[name = tensor("op_236"), val = tensor([1, 1])]; + tensor var_238 = const()[name = tensor("op_238"), 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 encoder_down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13955776)))]; + tensor encoder_down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14217984)))]; + tensor input_tensor_cast = conv(bias = encoder_down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_238, groups = var_17, pad = input_tensor_pad_0, pad_type = input_tensor_pad_type_0, strides = var_236, weight = encoder_down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16, x = input_57_cast)[name = tensor("input_tensor_cast")]; + tensor var_241_cast = add(x = input_tensor_cast, y = hidden_states_17_cast)[name = tensor("op_241_cast")]; + tensor reshape_40_shape_0 = const()[name = tensor("reshape_40_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_40_cast = reshape(shape = reshape_40_shape_0, x = var_241_cast)[name = tensor("reshape_40_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40_cast)[name = tensor("reduce_mean_30_cast")]; + tensor sub_20_cast = sub(x = reshape_40_cast, y = reduce_mean_30_cast)[name = tensor("sub_20_cast")]; + tensor square_10_cast = square(x = sub_20_cast)[name = tensor("square_10_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10_cast)[name = tensor("reduce_mean_32_cast")]; + tensor add_20_y_0_to_fp16 = const()[name = tensor("add_20_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_20_cast = add(x = reduce_mean_32_cast, y = add_20_y_0_to_fp16)[name = tensor("add_20_cast")]; + tensor sqrt_10_cast = sqrt(x = add_20_cast)[name = tensor("sqrt_10_cast")]; + tensor real_div_10_cast = real_div(x = sub_20_cast, y = sqrt_10_cast)[name = tensor("real_div_10_cast")]; + tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_41_cast = reshape(shape = reshape_41_shape_0, x = real_div_10_cast)[name = tensor("reshape_41_cast")]; + tensor add_21_gamma_0_to_fp16 = const()[name = tensor("add_21_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14219072)))]; + tensor add_21_beta_0_to_fp16 = const()[name = tensor("add_21_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14220160)))]; + tensor add_21_epsilon_0_to_fp16 = const()[name = tensor("add_21_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_21_cast = batch_norm(beta = add_21_beta_0_to_fp16, epsilon = add_21_epsilon_0_to_fp16, gamma = add_21_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_41_cast)[name = tensor("add_21_cast")]; + tensor input_75_cast = silu(x = add_21_cast)[name = tensor("input_75_cast")]; + tensor var_254 = const()[name = tensor("op_254"), val = tensor([1, 1])]; + tensor var_256 = const()[name = tensor("op_256"), val = tensor([1, 1])]; + tensor input_77_pad_type_0 = const()[name = tensor("input_77_pad_type_0"), val = tensor("custom")]; + tensor input_77_pad_0 = const()[name = tensor("input_77_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_2_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14221248)))]; + tensor encoder_down_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18939904)))]; + tensor input_77_cast = conv(bias = encoder_down_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_256, groups = var_17, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = var_254, weight = encoder_down_blocks_2_resnets_1_conv1_weight_to_fp16, x = input_75_cast)[name = tensor("input_77_cast")]; + tensor reshape_44_shape_0 = const()[name = tensor("reshape_44_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_44_cast = reshape(shape = reshape_44_shape_0, x = input_77_cast)[name = tensor("reshape_44_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44_cast)[name = tensor("reduce_mean_33_cast")]; + tensor sub_22_cast = sub(x = reshape_44_cast, y = reduce_mean_33_cast)[name = tensor("sub_22_cast")]; + tensor square_11_cast = square(x = sub_22_cast)[name = tensor("square_11_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11_cast)[name = tensor("reduce_mean_35_cast")]; + tensor add_22_y_0_to_fp16 = const()[name = tensor("add_22_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_22_cast = add(x = reduce_mean_35_cast, y = add_22_y_0_to_fp16)[name = tensor("add_22_cast")]; + tensor sqrt_11_cast = sqrt(x = add_22_cast)[name = tensor("sqrt_11_cast")]; + tensor real_div_11_cast = real_div(x = sub_22_cast, y = sqrt_11_cast)[name = tensor("real_div_11_cast")]; + tensor reshape_45_shape_0 = const()[name = tensor("reshape_45_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_45_cast = reshape(shape = reshape_45_shape_0, x = real_div_11_cast)[name = tensor("reshape_45_cast")]; + tensor add_23_gamma_0_to_fp16 = const()[name = tensor("add_23_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18940992)))]; + tensor add_23_beta_0_to_fp16 = const()[name = tensor("add_23_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18942080)))]; + tensor add_23_epsilon_0_to_fp16 = const()[name = tensor("add_23_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_23_cast = batch_norm(beta = add_23_beta_0_to_fp16, epsilon = add_23_epsilon_0_to_fp16, gamma = add_23_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_45_cast)[name = tensor("add_23_cast")]; + tensor input_81_cast = silu(x = add_23_cast)[name = tensor("input_81_cast")]; + tensor var_266 = const()[name = tensor("op_266"), val = tensor([1, 1])]; + tensor var_268 = const()[name = tensor("op_268"), 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 encoder_down_blocks_2_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18943168)))]; + tensor encoder_down_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23661824)))]; + tensor hidden_states_19_cast = conv(bias = encoder_down_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_268, groups = var_17, pad = hidden_states_19_pad_0, pad_type = hidden_states_19_pad_type_0, strides = var_266, weight = encoder_down_blocks_2_resnets_1_conv2_weight_to_fp16, x = input_81_cast)[name = tensor("hidden_states_19_cast")]; + tensor var_271_cast = add(x = var_241_cast, y = hidden_states_19_cast)[name = tensor("op_271_cast")]; + tensor hidden_states_23_pad_0 = const()[name = tensor("hidden_states_23_pad_0"), val = tensor([0, 0, 0, 0, 0, 1, 0, 1])]; + tensor hidden_states_23_mode_0 = const()[name = tensor("hidden_states_23_mode_0"), val = tensor("constant")]; + tensor hidden_states_23_constant_val_0_to_fp16 = const()[name = tensor("hidden_states_23_constant_val_0_to_fp16"), val = tensor(0x0p+0)]; + tensor hidden_states_23_cast = pad(constant_val = hidden_states_23_constant_val_0_to_fp16, mode = hidden_states_23_mode_0, pad = hidden_states_23_pad_0, x = var_271_cast)[name = tensor("hidden_states_23_cast")]; + tensor var_279 = const()[name = tensor("op_279"), val = tensor([2, 2])]; + tensor var_281 = const()[name = tensor("op_281"), val = tensor([1, 1])]; + tensor input_85_pad_type_0 = const()[name = tensor("input_85_pad_type_0"), val = tensor("custom")]; + tensor input_85_pad_0 = const()[name = tensor("input_85_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor encoder_down_blocks_2_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_2_downsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23662912)))]; + tensor encoder_down_blocks_2_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_2_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28381568)))]; + tensor input_85_cast = conv(bias = encoder_down_blocks_2_downsamplers_0_conv_bias_to_fp16, dilations = var_281, groups = var_17, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = var_279, weight = encoder_down_blocks_2_downsamplers_0_conv_weight_to_fp16, x = hidden_states_23_cast)[name = tensor("input_85_cast")]; + tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([1, 32, 16, 64, 64])]; + tensor reshape_48_cast = reshape(shape = reshape_48_shape_0, x = input_85_cast)[name = tensor("reshape_48_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48_cast)[name = tensor("reduce_mean_36_cast")]; + tensor sub_24_cast = sub(x = reshape_48_cast, y = reduce_mean_36_cast)[name = tensor("sub_24_cast")]; + tensor square_12_cast = square(x = sub_24_cast)[name = tensor("square_12_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12_cast)[name = tensor("reduce_mean_38_cast")]; + tensor add_24_y_0_to_fp16 = const()[name = tensor("add_24_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_24_cast = add(x = reduce_mean_38_cast, y = add_24_y_0_to_fp16)[name = tensor("add_24_cast")]; + tensor sqrt_12_cast = sqrt(x = add_24_cast)[name = tensor("sqrt_12_cast")]; + tensor real_div_12_cast = real_div(x = sub_24_cast, y = sqrt_12_cast)[name = tensor("real_div_12_cast")]; + tensor reshape_49_shape_0 = const()[name = tensor("reshape_49_shape_0"), val = tensor([1, 512, 64, 64])]; + tensor reshape_49_cast = reshape(shape = reshape_49_shape_0, x = real_div_12_cast)[name = tensor("reshape_49_cast")]; + tensor add_25_gamma_0_to_fp16 = const()[name = tensor("add_25_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28382656)))]; + tensor add_25_beta_0_to_fp16 = const()[name = tensor("add_25_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28383744)))]; + tensor add_25_epsilon_0_to_fp16 = const()[name = tensor("add_25_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_25_cast = batch_norm(beta = add_25_beta_0_to_fp16, epsilon = add_25_epsilon_0_to_fp16, gamma = add_25_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_49_cast)[name = tensor("add_25_cast")]; + tensor input_89_cast = silu(x = add_25_cast)[name = tensor("input_89_cast")]; + tensor var_298 = const()[name = tensor("op_298"), val = tensor([1, 1])]; + tensor var_300 = const()[name = tensor("op_300"), val = tensor([1, 1])]; + tensor input_91_pad_type_0 = const()[name = tensor("input_91_pad_type_0"), val = tensor("custom")]; + tensor input_91_pad_0 = const()[name = tensor("input_91_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_3_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_3_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28384832)))]; + tensor encoder_down_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33103488)))]; + tensor input_91_cast = conv(bias = encoder_down_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = var_300, groups = var_17, pad = input_91_pad_0, pad_type = input_91_pad_type_0, strides = var_298, weight = encoder_down_blocks_3_resnets_0_conv1_weight_to_fp16, x = input_89_cast)[name = tensor("input_91_cast")]; + tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([1, 32, 16, 64, 64])]; + tensor reshape_52_cast = reshape(shape = reshape_52_shape_0, x = input_91_cast)[name = tensor("reshape_52_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52_cast)[name = tensor("reduce_mean_39_cast")]; + tensor sub_26_cast = sub(x = reshape_52_cast, y = reduce_mean_39_cast)[name = tensor("sub_26_cast")]; + tensor square_13_cast = square(x = sub_26_cast)[name = tensor("square_13_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13_cast)[name = tensor("reduce_mean_41_cast")]; + tensor add_26_y_0_to_fp16 = const()[name = tensor("add_26_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_26_cast = add(x = reduce_mean_41_cast, y = add_26_y_0_to_fp16)[name = tensor("add_26_cast")]; + tensor sqrt_13_cast = sqrt(x = add_26_cast)[name = tensor("sqrt_13_cast")]; + tensor real_div_13_cast = real_div(x = sub_26_cast, y = sqrt_13_cast)[name = tensor("real_div_13_cast")]; + tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([1, 512, 64, 64])]; + tensor reshape_53_cast = reshape(shape = reshape_53_shape_0, x = real_div_13_cast)[name = tensor("reshape_53_cast")]; + tensor add_27_gamma_0_to_fp16 = const()[name = tensor("add_27_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33104576)))]; + tensor add_27_beta_0_to_fp16 = const()[name = tensor("add_27_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33105664)))]; + tensor add_27_epsilon_0_to_fp16 = const()[name = tensor("add_27_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_27_cast = batch_norm(beta = add_27_beta_0_to_fp16, epsilon = add_27_epsilon_0_to_fp16, gamma = add_27_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_53_cast)[name = tensor("add_27_cast")]; + tensor input_95_cast = silu(x = add_27_cast)[name = tensor("input_95_cast")]; + tensor var_310 = const()[name = tensor("op_310"), val = tensor([1, 1])]; + tensor var_312 = const()[name = tensor("op_312"), val = tensor([1, 1])]; + tensor hidden_states_25_pad_type_0 = const()[name = tensor("hidden_states_25_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_25_pad_0 = const()[name = tensor("hidden_states_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_3_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_3_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(33106752)))]; + tensor encoder_down_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37825408)))]; + tensor hidden_states_25_cast = conv(bias = encoder_down_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = var_312, groups = var_17, pad = hidden_states_25_pad_0, pad_type = hidden_states_25_pad_type_0, strides = var_310, weight = encoder_down_blocks_3_resnets_0_conv2_weight_to_fp16, x = input_95_cast)[name = tensor("hidden_states_25_cast")]; + tensor var_315_cast = add(x = input_85_cast, y = hidden_states_25_cast)[name = tensor("op_315_cast")]; + tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([1, 32, 16, 64, 64])]; + tensor reshape_56_cast = reshape(shape = reshape_56_shape_0, x = var_315_cast)[name = tensor("reshape_56_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56_cast)[name = tensor("reduce_mean_42_cast")]; + tensor sub_28_cast = sub(x = reshape_56_cast, y = reduce_mean_42_cast)[name = tensor("sub_28_cast")]; + tensor square_14_cast = square(x = sub_28_cast)[name = tensor("square_14_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14_cast)[name = tensor("reduce_mean_44_cast")]; + tensor add_28_y_0_to_fp16 = const()[name = tensor("add_28_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_28_cast = add(x = reduce_mean_44_cast, y = add_28_y_0_to_fp16)[name = tensor("add_28_cast")]; + tensor sqrt_14_cast = sqrt(x = add_28_cast)[name = tensor("sqrt_14_cast")]; + tensor real_div_14_cast = real_div(x = sub_28_cast, y = sqrt_14_cast)[name = tensor("real_div_14_cast")]; + tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([1, 512, 64, 64])]; + tensor reshape_57_cast = reshape(shape = reshape_57_shape_0, x = real_div_14_cast)[name = tensor("reshape_57_cast")]; + tensor add_29_gamma_0_to_fp16 = const()[name = tensor("add_29_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37826496)))]; + tensor add_29_beta_0_to_fp16 = const()[name = tensor("add_29_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37827584)))]; + tensor add_29_epsilon_0_to_fp16 = const()[name = tensor("add_29_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_29_cast = batch_norm(beta = add_29_beta_0_to_fp16, epsilon = add_29_epsilon_0_to_fp16, gamma = add_29_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_57_cast)[name = tensor("add_29_cast")]; + tensor input_103_cast = silu(x = add_29_cast)[name = tensor("input_103_cast")]; + tensor var_328 = const()[name = tensor("op_328"), val = tensor([1, 1])]; + tensor var_330 = const()[name = tensor("op_330"), val = tensor([1, 1])]; + tensor input_105_pad_type_0 = const()[name = tensor("input_105_pad_type_0"), val = tensor("custom")]; + tensor input_105_pad_0 = const()[name = tensor("input_105_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_down_blocks_3_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_3_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37828672)))]; + tensor encoder_down_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42547328)))]; + tensor input_105_cast = conv(bias = encoder_down_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = var_330, groups = var_17, pad = input_105_pad_0, pad_type = input_105_pad_type_0, strides = var_328, weight = encoder_down_blocks_3_resnets_1_conv1_weight_to_fp16, x = input_103_cast)[name = tensor("input_105_cast")]; + tensor reshape_60_shape_0 = const()[name = tensor("reshape_60_shape_0"), val = tensor([1, 32, 16, 64, 64])]; + tensor reshape_60_cast = reshape(shape = reshape_60_shape_0, x = input_105_cast)[name = tensor("reshape_60_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60_cast)[name = tensor("reduce_mean_45_cast")]; + tensor sub_30_cast = sub(x = reshape_60_cast, y = reduce_mean_45_cast)[name = tensor("sub_30_cast")]; + tensor square_15_cast = square(x = sub_30_cast)[name = tensor("square_15_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15_cast)[name = tensor("reduce_mean_47_cast")]; + tensor add_30_y_0_to_fp16 = const()[name = tensor("add_30_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_30_cast = add(x = reduce_mean_47_cast, y = add_30_y_0_to_fp16)[name = tensor("add_30_cast")]; + tensor sqrt_15_cast = sqrt(x = add_30_cast)[name = tensor("sqrt_15_cast")]; + tensor real_div_15_cast = real_div(x = sub_30_cast, y = sqrt_15_cast)[name = tensor("real_div_15_cast")]; + tensor reshape_61_shape_0 = const()[name = tensor("reshape_61_shape_0"), val = tensor([1, 512, 64, 64])]; + tensor reshape_61_cast = reshape(shape = reshape_61_shape_0, x = real_div_15_cast)[name = tensor("reshape_61_cast")]; + tensor add_31_gamma_0_to_fp16 = const()[name = tensor("add_31_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42548416)))]; + tensor add_31_beta_0_to_fp16 = const()[name = tensor("add_31_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42549504)))]; + tensor add_31_epsilon_0_to_fp16 = const()[name = tensor("add_31_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_31_cast = batch_norm(beta = add_31_beta_0_to_fp16, epsilon = add_31_epsilon_0_to_fp16, gamma = add_31_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_61_cast)[name = tensor("add_31_cast")]; + tensor input_109_cast = silu(x = add_31_cast)[name = tensor("input_109_cast")]; + tensor var_340 = const()[name = tensor("op_340"), val = tensor([1, 1])]; + tensor var_342 = const()[name = tensor("op_342"), 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 encoder_down_blocks_3_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("encoder_down_blocks_3_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42550592)))]; + tensor encoder_down_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("encoder_down_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47269248)))]; + tensor hidden_states_27_cast = conv(bias = encoder_down_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = var_342, groups = var_17, pad = hidden_states_27_pad_0, pad_type = hidden_states_27_pad_type_0, strides = var_340, weight = encoder_down_blocks_3_resnets_1_conv2_weight_to_fp16, x = input_109_cast)[name = tensor("hidden_states_27_cast")]; + tensor var_345_cast = add(x = var_315_cast, y = hidden_states_27_cast)[name = tensor("op_345_cast")]; + tensor reshape_64_shape_0 = const()[name = tensor("reshape_64_shape_0"), val = tensor([1, 32, 16, 64, 64])]; + tensor reshape_64_cast = reshape(shape = reshape_64_shape_0, x = var_345_cast)[name = tensor("reshape_64_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64_cast)[name = tensor("reduce_mean_48_cast")]; + tensor sub_32_cast = sub(x = reshape_64_cast, y = reduce_mean_48_cast)[name = tensor("sub_32_cast")]; + tensor square_16_cast = square(x = sub_32_cast)[name = tensor("square_16_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16_cast)[name = tensor("reduce_mean_50_cast")]; + tensor add_32_y_0_to_fp16 = const()[name = tensor("add_32_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_32_cast = add(x = reduce_mean_50_cast, y = add_32_y_0_to_fp16)[name = tensor("add_32_cast")]; + tensor sqrt_16_cast = sqrt(x = add_32_cast)[name = tensor("sqrt_16_cast")]; + tensor real_div_16_cast = real_div(x = sub_32_cast, y = sqrt_16_cast)[name = tensor("real_div_16_cast")]; + tensor reshape_65_shape_0 = const()[name = tensor("reshape_65_shape_0"), val = tensor([1, 512, 64, 64])]; + tensor reshape_65_cast = reshape(shape = reshape_65_shape_0, x = real_div_16_cast)[name = tensor("reshape_65_cast")]; + tensor add_33_gamma_0_to_fp16 = const()[name = tensor("add_33_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47270336)))]; + tensor add_33_beta_0_to_fp16 = const()[name = tensor("add_33_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47271424)))]; + tensor add_33_epsilon_0_to_fp16 = const()[name = tensor("add_33_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_33_cast = batch_norm(beta = add_33_beta_0_to_fp16, epsilon = add_33_epsilon_0_to_fp16, gamma = add_33_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_65_cast)[name = tensor("add_33_cast")]; + tensor input_117_cast = silu(x = add_33_cast)[name = tensor("input_117_cast")]; + tensor var_364 = const()[name = tensor("op_364"), val = tensor([1, 1])]; + tensor var_366 = const()[name = tensor("op_366"), val = tensor([1, 1])]; + tensor input_119_pad_type_0 = const()[name = tensor("input_119_pad_type_0"), val = tensor("custom")]; + tensor input_119_pad_0 = const()[name = tensor("input_119_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_mid_block_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("encoder_mid_block_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47272512)))]; + tensor encoder_mid_block_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("encoder_mid_block_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51991168)))]; + tensor input_119_cast = conv(bias = encoder_mid_block_resnets_0_conv1_bias_to_fp16, dilations = var_366, groups = var_17, pad = input_119_pad_0, pad_type = input_119_pad_type_0, strides = var_364, weight = encoder_mid_block_resnets_0_conv1_weight_to_fp16, x = input_117_cast)[name = tensor("input_119_cast")]; + tensor reshape_68_shape_0 = const()[name = tensor("reshape_68_shape_0"), val = tensor([1, 32, 16, 64, 64])]; + tensor reshape_68_cast = reshape(shape = reshape_68_shape_0, x = input_119_cast)[name = tensor("reshape_68_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68_cast)[name = tensor("reduce_mean_51_cast")]; + tensor sub_34_cast = sub(x = reshape_68_cast, y = reduce_mean_51_cast)[name = tensor("sub_34_cast")]; + tensor square_17_cast = square(x = sub_34_cast)[name = tensor("square_17_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17_cast)[name = tensor("reduce_mean_53_cast")]; + tensor add_34_y_0_to_fp16 = const()[name = tensor("add_34_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_34_cast = add(x = reduce_mean_53_cast, y = add_34_y_0_to_fp16)[name = tensor("add_34_cast")]; + tensor sqrt_17_cast = sqrt(x = add_34_cast)[name = tensor("sqrt_17_cast")]; + tensor real_div_17_cast = real_div(x = sub_34_cast, y = sqrt_17_cast)[name = tensor("real_div_17_cast")]; + tensor reshape_69_shape_0 = const()[name = tensor("reshape_69_shape_0"), val = tensor([1, 512, 64, 64])]; + tensor reshape_69_cast = reshape(shape = reshape_69_shape_0, x = real_div_17_cast)[name = tensor("reshape_69_cast")]; + tensor add_35_gamma_0_to_fp16 = const()[name = tensor("add_35_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51992256)))]; + tensor add_35_beta_0_to_fp16 = const()[name = tensor("add_35_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51993344)))]; + tensor add_35_epsilon_0_to_fp16 = const()[name = tensor("add_35_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_35_cast = batch_norm(beta = add_35_beta_0_to_fp16, epsilon = add_35_epsilon_0_to_fp16, gamma = add_35_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_69_cast)[name = tensor("add_35_cast")]; + tensor input_123_cast = silu(x = add_35_cast)[name = tensor("input_123_cast")]; + tensor var_376 = const()[name = tensor("op_376"), val = tensor([1, 1])]; + tensor var_378 = const()[name = tensor("op_378"), 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 encoder_mid_block_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("encoder_mid_block_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51994432)))]; + tensor encoder_mid_block_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("encoder_mid_block_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56713088)))]; + tensor hidden_states_29_cast = conv(bias = encoder_mid_block_resnets_0_conv2_bias_to_fp16, dilations = var_378, groups = var_17, pad = hidden_states_29_pad_0, pad_type = hidden_states_29_pad_type_0, strides = var_376, weight = encoder_mid_block_resnets_0_conv2_weight_to_fp16, x = input_123_cast)[name = tensor("hidden_states_29_cast")]; + tensor var_381_cast = add(x = var_345_cast, y = hidden_states_29_cast)[name = tensor("op_381_cast")]; + tensor reshape_72_shape_0 = const()[name = tensor("reshape_72_shape_0"), val = tensor([1, 32, 16, 64, 64])]; + tensor reshape_72_cast = reshape(shape = reshape_72_shape_0, x = var_381_cast)[name = tensor("reshape_72_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72_cast)[name = tensor("reduce_mean_54_cast")]; + tensor sub_36_cast = sub(x = reshape_72_cast, y = reduce_mean_54_cast)[name = tensor("sub_36_cast")]; + tensor square_18_cast = square(x = sub_36_cast)[name = tensor("square_18_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18_cast)[name = tensor("reduce_mean_56_cast")]; + tensor add_36_y_0_to_fp16 = const()[name = tensor("add_36_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_36_cast = add(x = reduce_mean_56_cast, y = add_36_y_0_to_fp16)[name = tensor("add_36_cast")]; + tensor sqrt_18_cast = sqrt(x = add_36_cast)[name = tensor("sqrt_18_cast")]; + tensor real_div_18_cast = real_div(x = sub_36_cast, y = sqrt_18_cast)[name = tensor("real_div_18_cast")]; + tensor reshape_73_shape_0 = const()[name = tensor("reshape_73_shape_0"), val = tensor([1, 512, 64, 64])]; + tensor reshape_73_cast = reshape(shape = reshape_73_shape_0, x = real_div_18_cast)[name = tensor("reshape_73_cast")]; + tensor add_37_gamma_0_to_fp16 = const()[name = tensor("add_37_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56714176)))]; + tensor add_37_beta_0_to_fp16 = const()[name = tensor("add_37_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56715264)))]; + tensor add_37_epsilon_0_to_fp16 = const()[name = tensor("add_37_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_37_cast = batch_norm(beta = add_37_beta_0_to_fp16, epsilon = add_37_epsilon_0_to_fp16, gamma = add_37_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_73_cast)[name = tensor("add_37_cast")]; + tensor var_400 = const()[name = tensor("op_400"), val = tensor([1, 512, 4096])]; + tensor var_401_cast = reshape(shape = var_400, x = add_37_cast)[name = tensor("op_401_cast")]; + tensor input_127_perm_0 = const()[name = tensor("input_127_perm_0"), val = tensor([0, 2, 1])]; + tensor encoder_mid_block_attentions_0_query_weight_to_fp16 = const()[name = tensor("encoder_mid_block_attentions_0_query_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56716352)))]; + tensor encoder_mid_block_attentions_0_query_bias_to_fp16 = const()[name = tensor("encoder_mid_block_attentions_0_query_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57240704)))]; + tensor transpose_6 = transpose(perm = input_127_perm_0, x = var_401_cast)[name = tensor("transpose_6")]; + tensor tensor_1_cast = linear(bias = encoder_mid_block_attentions_0_query_bias_to_fp16, weight = encoder_mid_block_attentions_0_query_weight_to_fp16, x = transpose_6)[name = tensor("tensor_1_cast")]; + tensor encoder_mid_block_attentions_0_key_weight_to_fp16 = const()[name = tensor("encoder_mid_block_attentions_0_key_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57241792)))]; + tensor encoder_mid_block_attentions_0_key_bias_to_fp16 = const()[name = tensor("encoder_mid_block_attentions_0_key_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57766144)))]; + tensor tensor_7_cast = linear(bias = encoder_mid_block_attentions_0_key_bias_to_fp16, weight = encoder_mid_block_attentions_0_key_weight_to_fp16, x = transpose_6)[name = tensor("tensor_7_cast")]; + tensor encoder_mid_block_attentions_0_value_weight_to_fp16 = const()[name = tensor("encoder_mid_block_attentions_0_value_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(57767232)))]; + tensor encoder_mid_block_attentions_0_value_bias_to_fp16 = const()[name = tensor("encoder_mid_block_attentions_0_value_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58291584)))]; + tensor tensor_13_cast = linear(bias = encoder_mid_block_attentions_0_value_bias_to_fp16, weight = encoder_mid_block_attentions_0_value_weight_to_fp16, x = transpose_6)[name = tensor("tensor_13_cast")]; + tensor var_419 = const()[name = tensor("op_419"), val = tensor([1, 4096, 1, 512])]; + tensor tensor_3_cast = reshape(shape = var_419, x = tensor_1_cast)[name = tensor("tensor_3_cast")]; + tensor var_421 = const()[name = tensor("op_421"), val = tensor([0, 2, 1, 3])]; + tensor var_428 = const()[name = tensor("op_428"), val = tensor([1, 4096, 512])]; + tensor transpose_5 = transpose(perm = var_421, x = tensor_3_cast)[name = tensor("transpose_5")]; + tensor query_proj_cast = reshape(shape = var_428, x = transpose_5)[name = tensor("query_proj_cast")]; + tensor var_437 = const()[name = tensor("op_437"), val = tensor([1, 4096, 1, 512])]; + tensor tensor_9_cast = reshape(shape = var_437, x = tensor_7_cast)[name = tensor("tensor_9_cast")]; + tensor var_439 = const()[name = tensor("op_439"), val = tensor([0, 2, 1, 3])]; + tensor var_446 = const()[name = tensor("op_446"), val = tensor([1, 4096, 512])]; + tensor transpose_4 = transpose(perm = var_439, x = tensor_9_cast)[name = tensor("transpose_4")]; + tensor key_proj_cast = reshape(shape = var_446, x = transpose_4)[name = tensor("key_proj_cast")]; + tensor var_455 = const()[name = tensor("op_455"), val = tensor([1, 4096, 1, 512])]; + tensor tensor_15_cast = reshape(shape = var_455, x = tensor_13_cast)[name = tensor("tensor_15_cast")]; + tensor var_457 = const()[name = tensor("op_457"), val = tensor([0, 2, 1, 3])]; + tensor var_464 = const()[name = tensor("op_464"), val = tensor([1, 4096, 512])]; + tensor transpose_3 = transpose(perm = var_457, x = tensor_15_cast)[name = tensor("transpose_3")]; + tensor value_proj_cast = reshape(shape = var_464, x = transpose_3)[name = tensor("value_proj_cast")]; + tensor var_471_perm_0 = const()[name = tensor("op_471_perm_0"), val = tensor([0, -1, -2])]; + tensor var_5_to_fp16 = const()[name = tensor("op_5_to_fp16"), val = tensor(0x1.6ap-5)]; + tensor query_proj_scaled_cast = mul(x = var_5_to_fp16, y = query_proj_cast)[name = tensor("query_proj_scaled_cast")]; + tensor attention_scores_1_bmm_transpose_x_0 = const()[name = tensor("attention_scores_1_bmm_transpose_x_0"), val = tensor(false)]; + tensor attention_scores_1_bmm_transpose_y_0 = const()[name = tensor("attention_scores_1_bmm_transpose_y_0"), val = tensor(false)]; + tensor transpose_2 = transpose(perm = var_471_perm_0, x = key_proj_cast)[name = tensor("transpose_2")]; + tensor attention_scores_1_bmm_cast = matmul(transpose_x = attention_scores_1_bmm_transpose_x_0, transpose_y = attention_scores_1_bmm_transpose_y_0, x = query_proj_scaled_cast, y = transpose_2)[name = tensor("attention_scores_1_bmm_cast")]; + tensor var_474_cast = softmax(axis = var_7, x = attention_scores_1_bmm_cast)[name = tensor("op_474_cast")]; + tensor tensor_19_transpose_x_0 = const()[name = tensor("tensor_19_transpose_x_0"), val = tensor(false)]; + tensor tensor_19_transpose_y_0 = const()[name = tensor("tensor_19_transpose_y_0"), val = tensor(false)]; + tensor tensor_19_cast = matmul(transpose_x = tensor_19_transpose_x_0, transpose_y = tensor_19_transpose_y_0, x = var_474_cast, y = value_proj_cast)[name = tensor("tensor_19_cast")]; + tensor var_485 = const()[name = tensor("op_485"), val = tensor([1, 1, 4096, 512])]; + tensor tensor_cast = reshape(shape = var_485, x = tensor_19_cast)[name = tensor("tensor_cast")]; + tensor var_487 = const()[name = tensor("op_487"), val = tensor([0, 2, 1, 3])]; + tensor var_492 = const()[name = tensor("op_492"), val = tensor([1, 4096, 512])]; + tensor transpose_1 = transpose(perm = var_487, x = tensor_cast)[name = tensor("transpose_1")]; + tensor input_129_cast = reshape(shape = var_492, x = transpose_1)[name = tensor("input_129_cast")]; + tensor encoder_mid_block_attentions_0_proj_attn_weight_to_fp16 = const()[name = tensor("encoder_mid_block_attentions_0_proj_attn_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58292672)))]; + tensor encoder_mid_block_attentions_0_proj_attn_bias_to_fp16 = const()[name = tensor("encoder_mid_block_attentions_0_proj_attn_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58817024)))]; + tensor hidden_states_35_cast = linear(bias = encoder_mid_block_attentions_0_proj_attn_bias_to_fp16, weight = encoder_mid_block_attentions_0_proj_attn_weight_to_fp16, x = input_129_cast)[name = tensor("hidden_states_35_cast")]; + tensor var_497_perm_0 = const()[name = tensor("op_497_perm_0"), val = tensor([0, -1, -2])]; + tensor var_498 = const()[name = tensor("op_498"), val = tensor([1, 512, 64, 64])]; + tensor transpose_0 = transpose(perm = var_497_perm_0, x = hidden_states_35_cast)[name = tensor("transpose_0")]; + tensor hidden_states_37_cast = reshape(shape = var_498, x = transpose_0)[name = tensor("hidden_states_37_cast")]; + tensor var_500_cast = add(x = hidden_states_37_cast, y = var_381_cast)[name = tensor("op_500_cast")]; + tensor reshape_76_shape_0 = const()[name = tensor("reshape_76_shape_0"), val = tensor([1, 32, 16, 64, 64])]; + tensor reshape_76_cast = reshape(shape = reshape_76_shape_0, x = var_500_cast)[name = tensor("reshape_76_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76_cast)[name = tensor("reduce_mean_57_cast")]; + tensor sub_38_cast = sub(x = reshape_76_cast, y = reduce_mean_57_cast)[name = tensor("sub_38_cast")]; + tensor square_19_cast = square(x = sub_38_cast)[name = tensor("square_19_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19_cast)[name = tensor("reduce_mean_59_cast")]; + tensor add_38_y_0_to_fp16 = const()[name = tensor("add_38_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_38_cast = add(x = reduce_mean_59_cast, y = add_38_y_0_to_fp16)[name = tensor("add_38_cast")]; + tensor sqrt_19_cast = sqrt(x = add_38_cast)[name = tensor("sqrt_19_cast")]; + tensor real_div_19_cast = real_div(x = sub_38_cast, y = sqrt_19_cast)[name = tensor("real_div_19_cast")]; + tensor reshape_77_shape_0 = const()[name = tensor("reshape_77_shape_0"), val = tensor([1, 512, 64, 64])]; + tensor reshape_77_cast = reshape(shape = reshape_77_shape_0, x = real_div_19_cast)[name = tensor("reshape_77_cast")]; + tensor add_39_gamma_0_to_fp16 = const()[name = tensor("add_39_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58818112)))]; + tensor add_39_beta_0_to_fp16 = const()[name = tensor("add_39_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58819200)))]; + tensor add_39_epsilon_0_to_fp16 = const()[name = tensor("add_39_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_39_cast = batch_norm(beta = add_39_beta_0_to_fp16, epsilon = add_39_epsilon_0_to_fp16, gamma = add_39_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_77_cast)[name = tensor("add_39_cast")]; + tensor input_135_cast = silu(x = add_39_cast)[name = tensor("input_135_cast")]; + tensor var_513 = const()[name = tensor("op_513"), val = tensor([1, 1])]; + tensor var_515 = const()[name = tensor("op_515"), val = tensor([1, 1])]; + tensor input_137_pad_type_0 = const()[name = tensor("input_137_pad_type_0"), val = tensor("custom")]; + tensor input_137_pad_0 = const()[name = tensor("input_137_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_mid_block_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("encoder_mid_block_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58820288)))]; + tensor encoder_mid_block_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("encoder_mid_block_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63538944)))]; + tensor input_137_cast = conv(bias = encoder_mid_block_resnets_1_conv1_bias_to_fp16, dilations = var_515, groups = var_17, pad = input_137_pad_0, pad_type = input_137_pad_type_0, strides = var_513, weight = encoder_mid_block_resnets_1_conv1_weight_to_fp16, x = input_135_cast)[name = tensor("input_137_cast")]; + tensor reshape_80_shape_0 = const()[name = tensor("reshape_80_shape_0"), val = tensor([1, 32, 16, 64, 64])]; + tensor reshape_80_cast = reshape(shape = reshape_80_shape_0, x = input_137_cast)[name = tensor("reshape_80_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80_cast)[name = tensor("reduce_mean_60_cast")]; + tensor sub_40_cast = sub(x = reshape_80_cast, y = reduce_mean_60_cast)[name = tensor("sub_40_cast")]; + tensor square_20_cast = square(x = sub_40_cast)[name = tensor("square_20_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20_cast)[name = tensor("reduce_mean_62_cast")]; + tensor add_40_y_0_to_fp16 = const()[name = tensor("add_40_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_40_cast = add(x = reduce_mean_62_cast, y = add_40_y_0_to_fp16)[name = tensor("add_40_cast")]; + tensor sqrt_20_cast = sqrt(x = add_40_cast)[name = tensor("sqrt_20_cast")]; + tensor real_div_20_cast = real_div(x = sub_40_cast, y = sqrt_20_cast)[name = tensor("real_div_20_cast")]; + tensor reshape_81_shape_0 = const()[name = tensor("reshape_81_shape_0"), val = tensor([1, 512, 64, 64])]; + tensor reshape_81_cast = reshape(shape = reshape_81_shape_0, x = real_div_20_cast)[name = tensor("reshape_81_cast")]; + tensor add_41_gamma_0_to_fp16 = const()[name = tensor("add_41_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63540032)))]; + tensor add_41_beta_0_to_fp16 = const()[name = tensor("add_41_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63541120)))]; + tensor add_41_epsilon_0_to_fp16 = const()[name = tensor("add_41_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_41_cast = batch_norm(beta = add_41_beta_0_to_fp16, epsilon = add_41_epsilon_0_to_fp16, gamma = add_41_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_81_cast)[name = tensor("add_41_cast")]; + tensor input_141_cast = silu(x = add_41_cast)[name = tensor("input_141_cast")]; + tensor var_525 = const()[name = tensor("op_525"), val = tensor([1, 1])]; + tensor var_527 = const()[name = tensor("op_527"), 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 encoder_mid_block_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("encoder_mid_block_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63542208)))]; + tensor encoder_mid_block_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("encoder_mid_block_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68260864)))]; + tensor hidden_states_cast = conv(bias = encoder_mid_block_resnets_1_conv2_bias_to_fp16, dilations = var_527, groups = var_17, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_525, weight = encoder_mid_block_resnets_1_conv2_weight_to_fp16, x = input_141_cast)[name = tensor("hidden_states_cast")]; + tensor var_530_cast = add(x = var_500_cast, y = hidden_states_cast)[name = tensor("op_530_cast")]; + tensor reshape_84_shape_0 = const()[name = tensor("reshape_84_shape_0"), val = tensor([1, 32, 16, 64, 64])]; + tensor reshape_84_cast = reshape(shape = reshape_84_shape_0, x = var_530_cast)[name = tensor("reshape_84_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84_cast)[name = tensor("reduce_mean_63_cast")]; + tensor sub_42_cast = sub(x = reshape_84_cast, y = reduce_mean_63_cast)[name = tensor("sub_42_cast")]; + tensor square_21_cast = square(x = sub_42_cast)[name = tensor("square_21_cast")]; + 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_cast = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21_cast)[name = tensor("reduce_mean_65_cast")]; + tensor add_42_y_0_to_fp16 = const()[name = tensor("add_42_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_42_cast = add(x = reduce_mean_65_cast, y = add_42_y_0_to_fp16)[name = tensor("add_42_cast")]; + tensor sqrt_21_cast = sqrt(x = add_42_cast)[name = tensor("sqrt_21_cast")]; + tensor real_div_21_cast = real_div(x = sub_42_cast, y = sqrt_21_cast)[name = tensor("real_div_21_cast")]; + tensor reshape_85_shape_0 = const()[name = tensor("reshape_85_shape_0"), val = tensor([1, 512, 64, 64])]; + tensor reshape_85_cast = reshape(shape = reshape_85_shape_0, x = real_div_21_cast)[name = tensor("reshape_85_cast")]; + tensor add_43_gamma_0_to_fp16 = const()[name = tensor("add_43_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68261952)))]; + tensor add_43_beta_0_to_fp16 = const()[name = tensor("add_43_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68263040)))]; + tensor add_43_epsilon_0_to_fp16 = const()[name = tensor("add_43_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_43_cast = batch_norm(beta = add_43_beta_0_to_fp16, epsilon = add_43_epsilon_0_to_fp16, gamma = add_43_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_85_cast)[name = tensor("add_43_cast")]; + tensor input_149_cast = silu(x = add_43_cast)[name = tensor("input_149_cast")]; + tensor var_539 = const()[name = tensor("op_539"), val = tensor([1, 1])]; + tensor var_541 = const()[name = tensor("op_541"), val = tensor([1, 1])]; + tensor input_pad_type_0 = const()[name = tensor("input_pad_type_0"), val = tensor("custom")]; + tensor input_pad_0 = const()[name = tensor("input_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor encoder_conv_out_weight_to_fp16 = const()[name = tensor("encoder_conv_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68264128)))]; + tensor encoder_conv_out_bias_to_fp16 = const()[name = tensor("encoder_conv_out_bias_to_fp16"), val = tensor([-0x1.734p-9, 0x1.0f4p-8, 0x1.afp-6, -0x1.494p-7, -0x1.ep-9, -0x1.924p-8, -0x1.1dp-10, -0x1.4b8p-8])]; + tensor input_cast = conv(bias = encoder_conv_out_bias_to_fp16, dilations = var_541, groups = var_17, pad = input_pad_0, pad_type = input_pad_type_0, strides = var_539, weight = encoder_conv_out_weight_to_fp16, x = input_149_cast)[name = tensor("input_cast")]; + tensor var_547 = const()[name = tensor("op_547"), val = tensor(1)]; + tensor var_550 = const()[name = tensor("op_550"), val = tensor([1, 1])]; + tensor var_552 = const()[name = tensor("op_552"), val = tensor([1, 1])]; + tensor var_554_pad_type_0 = const()[name = tensor("op_554_pad_type_0"), val = tensor("custom")]; + tensor var_554_pad_0 = const()[name = tensor("op_554_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor quant_conv_weight_to_fp16 = const()[name = tensor("quant_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68337920)))]; + tensor quant_conv_bias_to_fp16 = const()[name = tensor("quant_conv_bias_to_fp16"), val = tensor([0x1.8cp-3, 0x1.d68p-4, -0x1.b8cp-4, -0x1.5fp-2, -0x1.284p+1, -0x1.09cp+1, -0x1.178p+1, -0x1.1d8p+1])]; + tensor var_554_cast = conv(bias = quant_conv_bias_to_fp16, dilations = var_552, groups = var_547, pad = var_554_pad_0, pad_type = var_554_pad_type_0, strides = var_550, weight = quant_conv_weight_to_fp16, x = input_cast)[name = tensor("op_554_cast")]; + tensor var_554_cast_to_fp32_dtype_0 = const()[name = tensor("op_554_cast_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor latent = cast(dtype = var_554_cast_to_fp32_dtype_0, x = var_554_cast)[name = tensor("cast_34")]; + } -> (latent); +} \ No newline at end of file