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program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "4.28.4"}, {"coremlc-version", "1436.100.10"}, {"coremltools-component-torch", "2.0.0"}, {"coremltools-version", "6.3.0"}})]
{
func main<ios16>(tensor<fp16, [1, 3, 384, 640]> z) {
tensor<int32, []> var_7 = const()[name = tensor<string, []>("op_7"), val = tensor<int32, []>(-1)];
tensor<int32, []> var_17 = const()[name = tensor<string, []>("op_17"), val = tensor<int32, []>(1)];
tensor<int32, [2]> var_35 = const()[name = tensor<string, []>("op_35"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_37 = const()[name = tensor<string, []>("op_37"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_1_pad_type_0 = const()[name = tensor<string, []>("input_1_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_1_pad_0 = const()[name = tensor<string, []>("input_1_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<fp16, [128, 3, 3, 3]> encoder_conv_in_weight_to_fp16 = const()[name = tensor<string, []>("encoder_conv_in_weight_to_fp16"), val = tensor<fp16, [128, 3, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
tensor<fp16, [128]> encoder_conv_in_bias_to_fp16 = const()[name = tensor<string, []>("encoder_conv_in_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7040)))];
tensor<fp16, [1, 128, 384, 640]> 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);
tensor<int32, [5]> reshape_0_shape_0 = const()[name = tensor<string, []>("reshape_0_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 384, 640])];
tensor<fp16, [1, 32, 4, 384, 640]> reshape_0_cast = reshape(shape = reshape_0_shape_0, x = input_1_cast);
tensor<int32, [3]> reduce_mean_0_axes_0 = const()[name = tensor<string, []>("reduce_mean_0_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_0_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_0_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_0_cast = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0_cast);
tensor<fp16, [1, 32, 4, 384, 640]> sub_0_cast = sub(x = reshape_0_cast, y = reduce_mean_0_cast);
tensor<fp16, [1, 32, 4, 384, 640]> square_0_cast = square(x = sub_0_cast);
tensor<int32, [3]> reduce_mean_2_axes_0 = const()[name = tensor<string, []>("reduce_mean_2_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_2_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_2_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_2_cast = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0_cast);
tensor<fp16, []> add_0_y_0_to_fp16 = const()[name = tensor<string, []>("add_0_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_0_cast = add(x = reduce_mean_2_cast, y = add_0_y_0_to_fp16);
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_0_cast = sqrt(x = add_0_cast);
tensor<fp16, [1, 32, 4, 384, 640]> real_div_0_cast = real_div(x = sub_0_cast, y = sqrt_0_cast);
tensor<int32, [4]> reshape_1_shape_0 = const()[name = tensor<string, []>("reshape_1_shape_0"), val = tensor<int32, [4]>([1, 128, 384, 640])];
tensor<fp16, [1, 128, 384, 640]> reshape_1_cast = reshape(shape = reshape_1_shape_0, x = real_div_0_cast);
tensor<fp16, [128]> add_1_mean_0_to_fp16 = const()[name = tensor<string, []>("add_1_mean_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7360)))];
tensor<fp16, [128]> add_1_variance_0_to_fp16 = const()[name = tensor<string, []>("add_1_variance_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7680)))];
tensor<fp16, [128]> add_1_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_1_gamma_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8000)))];
tensor<fp16, [128]> add_1_beta_0_to_fp16 = const()[name = tensor<string, []>("add_1_beta_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8320)))];
tensor<fp16, []> add_1_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_1_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 128, 384, 640]> 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);
tensor<fp16, [1, 128, 384, 640]> input_5_cast = silu(x = add_1_cast);
tensor<int32, [2]> var_56 = const()[name = tensor<string, []>("op_56"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_58 = const()[name = tensor<string, []>("op_58"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_7_pad_type_0 = const()[name = tensor<string, []>("input_7_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_7_pad_0 = const()[name = tensor<string, []>("input_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<fp16, [128, 128, 3, 3]> encoder_down_blocks_0_resnets_0_conv1_weight_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_0_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8640)))];
tensor<fp16, [128]> encoder_down_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(303616)))];
tensor<fp16, [1, 128, 384, 640]> 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);
tensor<int32, [5]> reshape_4_shape_0 = const()[name = tensor<string, []>("reshape_4_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 384, 640])];
tensor<fp16, [1, 32, 4, 384, 640]> reshape_4_cast = reshape(shape = reshape_4_shape_0, x = input_7_cast);
tensor<int32, [3]> reduce_mean_3_axes_0 = const()[name = tensor<string, []>("reduce_mean_3_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_3_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_3_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_3_cast = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4_cast);
tensor<fp16, [1, 32, 4, 384, 640]> sub_2_cast = sub(x = reshape_4_cast, y = reduce_mean_3_cast);
tensor<fp16, [1, 32, 4, 384, 640]> square_1_cast = square(x = sub_2_cast);
tensor<int32, [3]> reduce_mean_5_axes_0 = const()[name = tensor<string, []>("reduce_mean_5_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_5_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_5_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_5_cast = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1_cast);
tensor<fp16, []> add_2_y_0_to_fp16 = const()[name = tensor<string, []>("add_2_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_2_cast = add(x = reduce_mean_5_cast, y = add_2_y_0_to_fp16);
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_1_cast = sqrt(x = add_2_cast);
tensor<fp16, [1, 32, 4, 384, 640]> real_div_1_cast = real_div(x = sub_2_cast, y = sqrt_1_cast);
tensor<int32, [4]> reshape_5_shape_0 = const()[name = tensor<string, []>("reshape_5_shape_0"), val = tensor<int32, [4]>([1, 128, 384, 640])];
tensor<fp16, [1, 128, 384, 640]> reshape_5_cast = reshape(shape = reshape_5_shape_0, x = real_div_1_cast);
tensor<fp16, [128]> add_3_mean_0_to_fp16 = const()[name = tensor<string, []>("add_3_mean_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(303936)))];
tensor<fp16, [128]> add_3_variance_0_to_fp16 = const()[name = tensor<string, []>("add_3_variance_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(304256)))];
tensor<fp16, [128]> add_3_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_3_gamma_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(304576)))];
tensor<fp16, [128]> add_3_beta_0_to_fp16 = const()[name = tensor<string, []>("add_3_beta_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(304896)))];
tensor<fp16, []> add_3_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_3_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 128, 384, 640]> 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_3_mean_0_to_fp16, variance = add_3_variance_0_to_fp16, x = reshape_5_cast);
tensor<fp16, [1, 128, 384, 640]> input_11_cast = silu(x = add_3_cast);
tensor<int32, [2]> var_68 = const()[name = tensor<string, []>("op_68"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_70 = const()[name = tensor<string, []>("op_70"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_1_pad_type_0 = const()[name = tensor<string, []>("hidden_states_1_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_1_pad_0 = const()[name = tensor<string, []>("hidden_states_1_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<fp16, [128, 128, 3, 3]> encoder_down_blocks_0_resnets_0_conv2_weight_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_0_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(305216)))];
tensor<fp16, [128]> encoder_down_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(600192)))];
tensor<fp16, [1, 128, 384, 640]> 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);
tensor<fp16, [1, 128, 384, 640]> var_73_cast = add(x = input_1_cast, y = hidden_states_1_cast);
tensor<int32, [5]> reshape_8_shape_0 = const()[name = tensor<string, []>("reshape_8_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 384, 640])];
tensor<fp16, [1, 32, 4, 384, 640]> reshape_8_cast = reshape(shape = reshape_8_shape_0, x = var_73_cast);
tensor<int32, [3]> reduce_mean_6_axes_0 = const()[name = tensor<string, []>("reduce_mean_6_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_6_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_6_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_6_cast = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8_cast);
tensor<fp16, [1, 32, 4, 384, 640]> sub_4_cast = sub(x = reshape_8_cast, y = reduce_mean_6_cast);
tensor<fp16, [1, 32, 4, 384, 640]> square_2_cast = square(x = sub_4_cast);
tensor<int32, [3]> reduce_mean_8_axes_0 = const()[name = tensor<string, []>("reduce_mean_8_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_8_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_8_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_8_cast = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2_cast);
tensor<fp16, []> add_4_y_0_to_fp16 = const()[name = tensor<string, []>("add_4_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_4_cast = add(x = reduce_mean_8_cast, y = add_4_y_0_to_fp16);
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_2_cast = sqrt(x = add_4_cast);
tensor<fp16, [1, 32, 4, 384, 640]> real_div_2_cast = real_div(x = sub_4_cast, y = sqrt_2_cast);
tensor<int32, [4]> reshape_9_shape_0 = const()[name = tensor<string, []>("reshape_9_shape_0"), val = tensor<int32, [4]>([1, 128, 384, 640])];
tensor<fp16, [1, 128, 384, 640]> reshape_9_cast = reshape(shape = reshape_9_shape_0, x = real_div_2_cast);
tensor<fp16, [128]> add_5_mean_0_to_fp16 = const()[name = tensor<string, []>("add_5_mean_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(600512)))];
tensor<fp16, [128]> add_5_variance_0_to_fp16 = const()[name = tensor<string, []>("add_5_variance_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(600832)))];
tensor<fp16, [128]> add_5_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_5_gamma_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(601152)))];
tensor<fp16, [128]> add_5_beta_0_to_fp16 = const()[name = tensor<string, []>("add_5_beta_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(601472)))];
tensor<fp16, []> add_5_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_5_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 128, 384, 640]> 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_5_mean_0_to_fp16, variance = add_5_variance_0_to_fp16, x = reshape_9_cast);
tensor<fp16, [1, 128, 384, 640]> input_19_cast = silu(x = add_5_cast);
tensor<int32, [2]> var_86 = const()[name = tensor<string, []>("op_86"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_88 = const()[name = tensor<string, []>("op_88"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_21_pad_type_0 = const()[name = tensor<string, []>("input_21_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_21_pad_0 = const()[name = tensor<string, []>("input_21_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<fp16, [128, 128, 3, 3]> encoder_down_blocks_0_resnets_1_conv1_weight_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_0_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(601792)))];
tensor<fp16, [128]> encoder_down_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(896768)))];
tensor<fp16, [1, 128, 384, 640]> 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);
tensor<int32, [5]> reshape_12_shape_0 = const()[name = tensor<string, []>("reshape_12_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 384, 640])];
tensor<fp16, [1, 32, 4, 384, 640]> reshape_12_cast = reshape(shape = reshape_12_shape_0, x = input_21_cast);
tensor<int32, [3]> reduce_mean_9_axes_0 = const()[name = tensor<string, []>("reduce_mean_9_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_9_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_9_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_9_cast = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12_cast);
tensor<fp16, [1, 32, 4, 384, 640]> sub_6_cast = sub(x = reshape_12_cast, y = reduce_mean_9_cast);
tensor<fp16, [1, 32, 4, 384, 640]> square_3_cast = square(x = sub_6_cast);
tensor<int32, [3]> reduce_mean_11_axes_0 = const()[name = tensor<string, []>("reduce_mean_11_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_11_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_11_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_11_cast = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3_cast);
tensor<fp16, []> add_6_y_0_to_fp16 = const()[name = tensor<string, []>("add_6_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_6_cast = add(x = reduce_mean_11_cast, y = add_6_y_0_to_fp16);
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_3_cast = sqrt(x = add_6_cast);
tensor<fp16, [1, 32, 4, 384, 640]> real_div_3_cast = real_div(x = sub_6_cast, y = sqrt_3_cast);
tensor<int32, [4]> reshape_13_shape_0 = const()[name = tensor<string, []>("reshape_13_shape_0"), val = tensor<int32, [4]>([1, 128, 384, 640])];
tensor<fp16, [1, 128, 384, 640]> reshape_13_cast = reshape(shape = reshape_13_shape_0, x = real_div_3_cast);
tensor<fp16, [128]> add_7_mean_0_to_fp16 = const()[name = tensor<string, []>("add_7_mean_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(897088)))];
tensor<fp16, [128]> add_7_variance_0_to_fp16 = const()[name = tensor<string, []>("add_7_variance_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(897408)))];
tensor<fp16, [128]> add_7_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_7_gamma_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(897728)))];
tensor<fp16, [128]> add_7_beta_0_to_fp16 = const()[name = tensor<string, []>("add_7_beta_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(898048)))];
tensor<fp16, []> add_7_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_7_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 128, 384, 640]> 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_7_mean_0_to_fp16, variance = add_7_variance_0_to_fp16, x = reshape_13_cast);
tensor<fp16, [1, 128, 384, 640]> input_25_cast = silu(x = add_7_cast);
tensor<int32, [2]> var_98 = const()[name = tensor<string, []>("op_98"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_100 = const()[name = tensor<string, []>("op_100"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_3_pad_type_0 = const()[name = tensor<string, []>("hidden_states_3_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_3_pad_0 = const()[name = tensor<string, []>("hidden_states_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<fp16, [128, 128, 3, 3]> encoder_down_blocks_0_resnets_1_conv2_weight_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_0_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(898368)))];
tensor<fp16, [128]> encoder_down_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1193344)))];
tensor<fp16, [1, 128, 384, 640]> 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);
tensor<fp16, [1, 128, 384, 640]> var_103_cast = add(x = var_73_cast, y = hidden_states_3_cast);
tensor<int32, [8]> hidden_states_7_pad_0 = const()[name = tensor<string, []>("hidden_states_7_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 1, 0, 1])];
tensor<string, []> hidden_states_7_mode_0 = const()[name = tensor<string, []>("hidden_states_7_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> hidden_states_7_constant_val_0_to_fp16 = const()[name = tensor<string, []>("hidden_states_7_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 128, 385, 641]> 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);
tensor<int32, [2]> var_111 = const()[name = tensor<string, []>("op_111"), val = tensor<int32, [2]>([2, 2])];
tensor<int32, [2]> var_113 = const()[name = tensor<string, []>("op_113"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_29_pad_type_0 = const()[name = tensor<string, []>("input_29_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_29_pad_0 = const()[name = tensor<string, []>("input_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [128, 128, 3, 3]> encoder_down_blocks_0_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_0_downsamplers_0_conv_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1193664)))];
tensor<fp16, [128]> encoder_down_blocks_0_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_0_downsamplers_0_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1488640)))];
tensor<fp16, [1, 128, 192, 320]> 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);
tensor<int32, [5]> reshape_16_shape_0 = const()[name = tensor<string, []>("reshape_16_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 192, 320])];
tensor<fp16, [1, 32, 4, 192, 320]> reshape_16_cast = reshape(shape = reshape_16_shape_0, x = input_29_cast);
tensor<int32, [3]> reduce_mean_12_axes_0 = const()[name = tensor<string, []>("reduce_mean_12_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_12_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_12_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_12_cast = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16_cast);
tensor<fp16, [1, 32, 4, 192, 320]> sub_8_cast = sub(x = reshape_16_cast, y = reduce_mean_12_cast);
tensor<fp16, [1, 32, 4, 192, 320]> square_4_cast = square(x = sub_8_cast);
tensor<int32, [3]> reduce_mean_14_axes_0 = const()[name = tensor<string, []>("reduce_mean_14_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_14_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_14_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_14_cast = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4_cast);
tensor<fp16, []> add_8_y_0_to_fp16 = const()[name = tensor<string, []>("add_8_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_8_cast = add(x = reduce_mean_14_cast, y = add_8_y_0_to_fp16);
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_4_cast = sqrt(x = add_8_cast);
tensor<fp16, [1, 32, 4, 192, 320]> real_div_4_cast = real_div(x = sub_8_cast, y = sqrt_4_cast);
tensor<int32, [4]> reshape_17_shape_0 = const()[name = tensor<string, []>("reshape_17_shape_0"), val = tensor<int32, [4]>([1, 128, 192, 320])];
tensor<fp16, [1, 128, 192, 320]> reshape_17_cast = reshape(shape = reshape_17_shape_0, x = real_div_4_cast);
tensor<fp16, [128]> add_9_mean_0_to_fp16 = const()[name = tensor<string, []>("add_9_mean_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1488960)))];
tensor<fp16, [128]> add_9_variance_0_to_fp16 = const()[name = tensor<string, []>("add_9_variance_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1489280)))];
tensor<fp16, [128]> add_9_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_9_gamma_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1489600)))];
tensor<fp16, [128]> add_9_beta_0_to_fp16 = const()[name = tensor<string, []>("add_9_beta_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1489920)))];
tensor<fp16, []> add_9_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_9_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 128, 192, 320]> 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_9_mean_0_to_fp16, variance = add_9_variance_0_to_fp16, x = reshape_17_cast);
tensor<fp16, [1, 128, 192, 320]> input_33_cast = silu(x = add_9_cast);
tensor<int32, [2]> var_133 = const()[name = tensor<string, []>("op_133"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_135 = const()[name = tensor<string, []>("op_135"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_35_pad_type_0 = const()[name = tensor<string, []>("input_35_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_35_pad_0 = const()[name = tensor<string, []>("input_35_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<fp16, [256, 128, 3, 3]> encoder_down_blocks_1_resnets_0_conv1_weight_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_1_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [256, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1490240)))];
tensor<fp16, [256]> encoder_down_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2080128)))];
tensor<fp16, [1, 256, 192, 320]> 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);
tensor<int32, [5]> reshape_20_shape_0 = const()[name = tensor<string, []>("reshape_20_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 192, 320])];
tensor<fp16, [1, 32, 8, 192, 320]> reshape_20_cast = reshape(shape = reshape_20_shape_0, x = input_35_cast);
tensor<int32, [3]> reduce_mean_15_axes_0 = const()[name = tensor<string, []>("reduce_mean_15_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_15_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_15_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_15_cast = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20_cast);
tensor<fp16, [1, 32, 8, 192, 320]> sub_10_cast = sub(x = reshape_20_cast, y = reduce_mean_15_cast);
tensor<fp16, [1, 32, 8, 192, 320]> square_5_cast = square(x = sub_10_cast);
tensor<int32, [3]> reduce_mean_17_axes_0 = const()[name = tensor<string, []>("reduce_mean_17_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_17_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_17_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_17_cast = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5_cast);
tensor<fp16, []> add_10_y_0_to_fp16 = const()[name = tensor<string, []>("add_10_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_10_cast = add(x = reduce_mean_17_cast, y = add_10_y_0_to_fp16);
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_5_cast = sqrt(x = add_10_cast);
tensor<fp16, [1, 32, 8, 192, 320]> real_div_5_cast = real_div(x = sub_10_cast, y = sqrt_5_cast);
tensor<int32, [4]> reshape_21_shape_0 = const()[name = tensor<string, []>("reshape_21_shape_0"), val = tensor<int32, [4]>([1, 256, 192, 320])];
tensor<fp16, [1, 256, 192, 320]> reshape_21_cast = reshape(shape = reshape_21_shape_0, x = real_div_5_cast);
tensor<fp16, [256]> add_11_mean_0_to_fp16 = const()[name = tensor<string, []>("add_11_mean_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2080704)))];
tensor<fp16, [256]> add_11_variance_0_to_fp16 = const()[name = tensor<string, []>("add_11_variance_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2081280)))];
tensor<fp16, [256]> add_11_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_11_gamma_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2081856)))];
tensor<fp16, [256]> add_11_beta_0_to_fp16 = const()[name = tensor<string, []>("add_11_beta_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2082432)))];
tensor<fp16, []> add_11_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_11_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 256, 192, 320]> 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);
tensor<fp16, [1, 256, 192, 320]> input_39_cast = silu(x = add_11_cast);
tensor<int32, [2]> var_145 = const()[name = tensor<string, []>("op_145"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_147 = const()[name = tensor<string, []>("op_147"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_9_pad_type_0 = const()[name = tensor<string, []>("hidden_states_9_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_9_pad_0 = const()[name = tensor<string, []>("hidden_states_9_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<fp16, [256, 256, 3, 3]> encoder_down_blocks_1_resnets_0_conv2_weight_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_1_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2083008)))];
tensor<fp16, [256]> encoder_down_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3262720)))];
tensor<fp16, [1, 256, 192, 320]> 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);
tensor<int32, [2]> var_152 = const()[name = tensor<string, []>("op_152"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_154 = const()[name = tensor<string, []>("op_154"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_tensor_1_pad_type_0 = const()[name = tensor<string, []>("input_tensor_1_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_tensor_1_pad_0 = const()[name = tensor<string, []>("input_tensor_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [256, 128, 1, 1]> encoder_down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor<fp16, [256, 128, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3263296)))];
tensor<fp16, [256]> encoder_down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3328896)))];
tensor<fp16, [1, 256, 192, 320]> 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);
tensor<fp16, [1, 256, 192, 320]> var_157_cast = add(x = input_tensor_1_cast, y = hidden_states_9_cast);
tensor<int32, [5]> reshape_24_shape_0 = const()[name = tensor<string, []>("reshape_24_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 192, 320])];
tensor<fp16, [1, 32, 8, 192, 320]> reshape_24_cast = reshape(shape = reshape_24_shape_0, x = var_157_cast);
tensor<int32, [3]> reduce_mean_18_axes_0 = const()[name = tensor<string, []>("reduce_mean_18_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_18_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_18_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_18_cast = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24_cast);
tensor<fp16, [1, 32, 8, 192, 320]> sub_12_cast = sub(x = reshape_24_cast, y = reduce_mean_18_cast);
tensor<fp16, [1, 32, 8, 192, 320]> square_6_cast = square(x = sub_12_cast);
tensor<int32, [3]> reduce_mean_20_axes_0 = const()[name = tensor<string, []>("reduce_mean_20_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_20_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_20_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_20_cast = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6_cast);
tensor<fp16, []> add_12_y_0_to_fp16 = const()[name = tensor<string, []>("add_12_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_12_cast = add(x = reduce_mean_20_cast, y = add_12_y_0_to_fp16);
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_6_cast = sqrt(x = add_12_cast);
tensor<fp16, [1, 32, 8, 192, 320]> real_div_6_cast = real_div(x = sub_12_cast, y = sqrt_6_cast);
tensor<int32, [4]> reshape_25_shape_0 = const()[name = tensor<string, []>("reshape_25_shape_0"), val = tensor<int32, [4]>([1, 256, 192, 320])];
tensor<fp16, [1, 256, 192, 320]> reshape_25_cast = reshape(shape = reshape_25_shape_0, x = real_div_6_cast);
tensor<fp16, [256]> add_13_mean_0_to_fp16 = const()[name = tensor<string, []>("add_13_mean_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3329472)))];
tensor<fp16, [256]> add_13_variance_0_to_fp16 = const()[name = tensor<string, []>("add_13_variance_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3330048)))];
tensor<fp16, [256]> add_13_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_13_gamma_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3330624)))];
tensor<fp16, [256]> add_13_beta_0_to_fp16 = const()[name = tensor<string, []>("add_13_beta_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3331200)))];
tensor<fp16, []> add_13_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_13_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 256, 192, 320]> 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_13_mean_0_to_fp16, variance = add_13_variance_0_to_fp16, x = reshape_25_cast);
tensor<fp16, [1, 256, 192, 320]> input_47_cast = silu(x = add_13_cast);
tensor<int32, [2]> var_170 = const()[name = tensor<string, []>("op_170"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_172 = const()[name = tensor<string, []>("op_172"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_49_pad_type_0 = const()[name = tensor<string, []>("input_49_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_49_pad_0 = const()[name = tensor<string, []>("input_49_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<fp16, [256, 256, 3, 3]> encoder_down_blocks_1_resnets_1_conv1_weight_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_1_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3331776)))];
tensor<fp16, [256]> encoder_down_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4511488)))];
tensor<fp16, [1, 256, 192, 320]> 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);
tensor<int32, [5]> reshape_28_shape_0 = const()[name = tensor<string, []>("reshape_28_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 192, 320])];
tensor<fp16, [1, 32, 8, 192, 320]> reshape_28_cast = reshape(shape = reshape_28_shape_0, x = input_49_cast);
tensor<int32, [3]> reduce_mean_21_axes_0 = const()[name = tensor<string, []>("reduce_mean_21_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_21_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_21_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_21_cast = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28_cast);
tensor<fp16, [1, 32, 8, 192, 320]> sub_14_cast = sub(x = reshape_28_cast, y = reduce_mean_21_cast);
tensor<fp16, [1, 32, 8, 192, 320]> square_7_cast = square(x = sub_14_cast);
tensor<int32, [3]> reduce_mean_23_axes_0 = const()[name = tensor<string, []>("reduce_mean_23_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_23_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_23_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_23_cast = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7_cast);
tensor<fp16, []> add_14_y_0_to_fp16 = const()[name = tensor<string, []>("add_14_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_14_cast = add(x = reduce_mean_23_cast, y = add_14_y_0_to_fp16);
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_7_cast = sqrt(x = add_14_cast);
tensor<fp16, [1, 32, 8, 192, 320]> real_div_7_cast = real_div(x = sub_14_cast, y = sqrt_7_cast);
tensor<int32, [4]> reshape_29_shape_0 = const()[name = tensor<string, []>("reshape_29_shape_0"), val = tensor<int32, [4]>([1, 256, 192, 320])];
tensor<fp16, [1, 256, 192, 320]> reshape_29_cast = reshape(shape = reshape_29_shape_0, x = real_div_7_cast);
tensor<fp16, [256]> add_15_mean_0_to_fp16 = const()[name = tensor<string, []>("add_15_mean_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4512064)))];
tensor<fp16, [256]> add_15_variance_0_to_fp16 = const()[name = tensor<string, []>("add_15_variance_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4512640)))];
tensor<fp16, [256]> add_15_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_15_gamma_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4513216)))];
tensor<fp16, [256]> add_15_beta_0_to_fp16 = const()[name = tensor<string, []>("add_15_beta_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4513792)))];
tensor<fp16, []> add_15_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_15_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 256, 192, 320]> 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_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_29_cast);
tensor<fp16, [1, 256, 192, 320]> input_53_cast = silu(x = add_15_cast);
tensor<int32, [2]> var_182 = const()[name = tensor<string, []>("op_182"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_184 = const()[name = tensor<string, []>("op_184"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_11_pad_type_0 = const()[name = tensor<string, []>("hidden_states_11_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_11_pad_0 = const()[name = tensor<string, []>("hidden_states_11_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<fp16, [256, 256, 3, 3]> encoder_down_blocks_1_resnets_1_conv2_weight_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_1_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4514368)))];
tensor<fp16, [256]> encoder_down_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5694080)))];
tensor<fp16, [1, 256, 192, 320]> 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);
tensor<fp16, [1, 256, 192, 320]> var_187_cast = add(x = var_157_cast, y = hidden_states_11_cast);
tensor<int32, [8]> hidden_states_15_pad_0 = const()[name = tensor<string, []>("hidden_states_15_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 1, 0, 1])];
tensor<string, []> hidden_states_15_mode_0 = const()[name = tensor<string, []>("hidden_states_15_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> hidden_states_15_constant_val_0_to_fp16 = const()[name = tensor<string, []>("hidden_states_15_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 256, 193, 321]> 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);
tensor<int32, [2]> var_195 = const()[name = tensor<string, []>("op_195"), val = tensor<int32, [2]>([2, 2])];
tensor<int32, [2]> var_197 = const()[name = tensor<string, []>("op_197"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_57_pad_type_0 = const()[name = tensor<string, []>("input_57_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_57_pad_0 = const()[name = tensor<string, []>("input_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [256, 256, 3, 3]> encoder_down_blocks_1_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_1_downsamplers_0_conv_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5694656)))];
tensor<fp16, [256]> encoder_down_blocks_1_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_1_downsamplers_0_conv_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6874368)))];
tensor<fp16, [1, 256, 96, 160]> 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);
tensor<int32, [5]> reshape_32_shape_0 = const()[name = tensor<string, []>("reshape_32_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 96, 160])];
tensor<fp16, [1, 32, 8, 96, 160]> reshape_32_cast = reshape(shape = reshape_32_shape_0, x = input_57_cast);
tensor<int32, [3]> reduce_mean_24_axes_0 = const()[name = tensor<string, []>("reduce_mean_24_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_24_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_24_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_24_cast = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32_cast);
tensor<fp16, [1, 32, 8, 96, 160]> sub_16_cast = sub(x = reshape_32_cast, y = reduce_mean_24_cast);
tensor<fp16, [1, 32, 8, 96, 160]> square_8_cast = square(x = sub_16_cast);
tensor<int32, [3]> reduce_mean_26_axes_0 = const()[name = tensor<string, []>("reduce_mean_26_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_26_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_26_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_26_cast = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8_cast);
tensor<fp16, []> add_16_y_0_to_fp16 = const()[name = tensor<string, []>("add_16_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_16_cast = add(x = reduce_mean_26_cast, y = add_16_y_0_to_fp16);
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_8_cast = sqrt(x = add_16_cast);
tensor<fp16, [1, 32, 8, 96, 160]> real_div_8_cast = real_div(x = sub_16_cast, y = sqrt_8_cast);
tensor<int32, [4]> reshape_33_shape_0 = const()[name = tensor<string, []>("reshape_33_shape_0"), val = tensor<int32, [4]>([1, 256, 96, 160])];
tensor<fp16, [1, 256, 96, 160]> reshape_33_cast = reshape(shape = reshape_33_shape_0, x = real_div_8_cast);
tensor<fp16, [256]> add_17_mean_0_to_fp16 = const()[name = tensor<string, []>("add_17_mean_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6874944)))];
tensor<fp16, [256]> add_17_variance_0_to_fp16 = const()[name = tensor<string, []>("add_17_variance_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6875520)))];
tensor<fp16, [256]> add_17_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_17_gamma_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6876096)))];
tensor<fp16, [256]> add_17_beta_0_to_fp16 = const()[name = tensor<string, []>("add_17_beta_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6876672)))];
tensor<fp16, []> add_17_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_17_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 256, 96, 160]> 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_17_mean_0_to_fp16, variance = add_17_variance_0_to_fp16, x = reshape_33_cast);
tensor<fp16, [1, 256, 96, 160]> input_61_cast = silu(x = add_17_cast);
tensor<int32, [2]> var_217 = const()[name = tensor<string, []>("op_217"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_219 = const()[name = tensor<string, []>("op_219"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_63_pad_type_0 = const()[name = tensor<string, []>("input_63_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_63_pad_0 = const()[name = tensor<string, []>("input_63_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<fp16, [512, 256, 3, 3]> encoder_down_blocks_2_resnets_0_conv1_weight_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_2_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [512, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6877248)))];
tensor<fp16, [512]> encoder_down_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9236608)))];
tensor<fp16, [1, 512, 96, 160]> 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);
tensor<int32, [5]> reshape_36_shape_0 = const()[name = tensor<string, []>("reshape_36_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 96, 160])];
tensor<fp16, [1, 32, 16, 96, 160]> reshape_36_cast = reshape(shape = reshape_36_shape_0, x = input_63_cast);
tensor<int32, [3]> reduce_mean_27_axes_0 = const()[name = tensor<string, []>("reduce_mean_27_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_27_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_27_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_27_cast = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36_cast);
tensor<fp16, [1, 32, 16, 96, 160]> sub_18_cast = sub(x = reshape_36_cast, y = reduce_mean_27_cast);
tensor<fp16, [1, 32, 16, 96, 160]> square_9_cast = square(x = sub_18_cast);
tensor<int32, [3]> reduce_mean_29_axes_0 = const()[name = tensor<string, []>("reduce_mean_29_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_29_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_29_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_29_cast = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9_cast);
tensor<fp16, []> add_18_y_0_to_fp16 = const()[name = tensor<string, []>("add_18_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_18_cast = add(x = reduce_mean_29_cast, y = add_18_y_0_to_fp16);
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_9_cast = sqrt(x = add_18_cast);
tensor<fp16, [1, 32, 16, 96, 160]> real_div_9_cast = real_div(x = sub_18_cast, y = sqrt_9_cast);
tensor<int32, [4]> reshape_37_shape_0 = const()[name = tensor<string, []>("reshape_37_shape_0"), val = tensor<int32, [4]>([1, 512, 96, 160])];
tensor<fp16, [1, 512, 96, 160]> reshape_37_cast = reshape(shape = reshape_37_shape_0, x = real_div_9_cast);
tensor<fp16, [512]> add_19_mean_0_to_fp16 = const()[name = tensor<string, []>("add_19_mean_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9237696)))];
tensor<fp16, [512]> add_19_variance_0_to_fp16 = const()[name = tensor<string, []>("add_19_variance_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9238784)))];
tensor<fp16, [512]> add_19_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_19_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9239872)))];
tensor<fp16, [512]> add_19_beta_0_to_fp16 = const()[name = tensor<string, []>("add_19_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9240960)))];
tensor<fp16, []> add_19_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_19_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 96, 160]> 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);
tensor<fp16, [1, 512, 96, 160]> input_67_cast = silu(x = add_19_cast);
tensor<int32, [2]> var_229 = const()[name = tensor<string, []>("op_229"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_231 = const()[name = tensor<string, []>("op_231"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_17_pad_type_0 = const()[name = tensor<string, []>("hidden_states_17_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_17_pad_0 = const()[name = tensor<string, []>("hidden_states_17_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<fp16, [512, 512, 3, 3]> encoder_down_blocks_2_resnets_0_conv2_weight_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_2_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9242048)))];
tensor<fp16, [512]> encoder_down_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13960704)))];
tensor<fp16, [1, 512, 96, 160]> 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);
tensor<int32, [2]> var_236 = const()[name = tensor<string, []>("op_236"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_238 = const()[name = tensor<string, []>("op_238"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_tensor_pad_type_0 = const()[name = tensor<string, []>("input_tensor_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_tensor_pad_0 = const()[name = tensor<string, []>("input_tensor_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [512, 256, 1, 1]> encoder_down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor<fp16, [512, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13961792)))];
tensor<fp16, [512]> encoder_down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14224000)))];
tensor<fp16, [1, 512, 96, 160]> 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);
tensor<fp16, [1, 512, 96, 160]> var_241_cast = add(x = input_tensor_cast, y = hidden_states_17_cast);
tensor<int32, [5]> reshape_40_shape_0 = const()[name = tensor<string, []>("reshape_40_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 96, 160])];
tensor<fp16, [1, 32, 16, 96, 160]> reshape_40_cast = reshape(shape = reshape_40_shape_0, x = var_241_cast);
tensor<int32, [3]> reduce_mean_30_axes_0 = const()[name = tensor<string, []>("reduce_mean_30_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_30_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_30_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_30_cast = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40_cast);
tensor<fp16, [1, 32, 16, 96, 160]> sub_20_cast = sub(x = reshape_40_cast, y = reduce_mean_30_cast);
tensor<fp16, [1, 32, 16, 96, 160]> square_10_cast = square(x = sub_20_cast);
tensor<int32, [3]> reduce_mean_32_axes_0 = const()[name = tensor<string, []>("reduce_mean_32_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_32_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_32_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_32_cast = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10_cast);
tensor<fp16, []> add_20_y_0_to_fp16 = const()[name = tensor<string, []>("add_20_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_20_cast = add(x = reduce_mean_32_cast, y = add_20_y_0_to_fp16);
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_10_cast = sqrt(x = add_20_cast);
tensor<fp16, [1, 32, 16, 96, 160]> real_div_10_cast = real_div(x = sub_20_cast, y = sqrt_10_cast);
tensor<int32, [4]> reshape_41_shape_0 = const()[name = tensor<string, []>("reshape_41_shape_0"), val = tensor<int32, [4]>([1, 512, 96, 160])];
tensor<fp16, [1, 512, 96, 160]> reshape_41_cast = reshape(shape = reshape_41_shape_0, x = real_div_10_cast);
tensor<fp16, [512]> add_21_mean_0_to_fp16 = const()[name = tensor<string, []>("add_21_mean_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14225088)))];
tensor<fp16, [512]> add_21_variance_0_to_fp16 = const()[name = tensor<string, []>("add_21_variance_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14226176)))];
tensor<fp16, [512]> add_21_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_21_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14227264)))];
tensor<fp16, [512]> add_21_beta_0_to_fp16 = const()[name = tensor<string, []>("add_21_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14228352)))];
tensor<fp16, []> add_21_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_21_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 96, 160]> 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_21_mean_0_to_fp16, variance = add_21_variance_0_to_fp16, x = reshape_41_cast);
tensor<fp16, [1, 512, 96, 160]> input_75_cast = silu(x = add_21_cast);
tensor<int32, [2]> var_254 = const()[name = tensor<string, []>("op_254"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_256 = const()[name = tensor<string, []>("op_256"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_77_pad_type_0 = const()[name = tensor<string, []>("input_77_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_77_pad_0 = const()[name = tensor<string, []>("input_77_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<fp16, [512, 512, 3, 3]> encoder_down_blocks_2_resnets_1_conv1_weight_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_2_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14229440)))];
tensor<fp16, [512]> encoder_down_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18948096)))];
tensor<fp16, [1, 512, 96, 160]> 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);
tensor<int32, [5]> reshape_44_shape_0 = const()[name = tensor<string, []>("reshape_44_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 96, 160])];
tensor<fp16, [1, 32, 16, 96, 160]> reshape_44_cast = reshape(shape = reshape_44_shape_0, x = input_77_cast);
tensor<int32, [3]> reduce_mean_33_axes_0 = const()[name = tensor<string, []>("reduce_mean_33_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_33_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_33_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_33_cast = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44_cast);
tensor<fp16, [1, 32, 16, 96, 160]> sub_22_cast = sub(x = reshape_44_cast, y = reduce_mean_33_cast);
tensor<fp16, [1, 32, 16, 96, 160]> square_11_cast = square(x = sub_22_cast);
tensor<int32, [3]> reduce_mean_35_axes_0 = const()[name = tensor<string, []>("reduce_mean_35_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_35_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_35_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_35_cast = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11_cast);
tensor<fp16, []> add_22_y_0_to_fp16 = const()[name = tensor<string, []>("add_22_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_22_cast = add(x = reduce_mean_35_cast, y = add_22_y_0_to_fp16);
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_11_cast = sqrt(x = add_22_cast);
tensor<fp16, [1, 32, 16, 96, 160]> real_div_11_cast = real_div(x = sub_22_cast, y = sqrt_11_cast);
tensor<int32, [4]> reshape_45_shape_0 = const()[name = tensor<string, []>("reshape_45_shape_0"), val = tensor<int32, [4]>([1, 512, 96, 160])];
tensor<fp16, [1, 512, 96, 160]> reshape_45_cast = reshape(shape = reshape_45_shape_0, x = real_div_11_cast);
tensor<fp16, [512]> add_23_mean_0_to_fp16 = const()[name = tensor<string, []>("add_23_mean_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18949184)))];
tensor<fp16, [512]> add_23_variance_0_to_fp16 = const()[name = tensor<string, []>("add_23_variance_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18950272)))];
tensor<fp16, [512]> add_23_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_23_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18951360)))];
tensor<fp16, [512]> add_23_beta_0_to_fp16 = const()[name = tensor<string, []>("add_23_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18952448)))];
tensor<fp16, []> add_23_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_23_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 96, 160]> 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_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_45_cast);
tensor<fp16, [1, 512, 96, 160]> input_81_cast = silu(x = add_23_cast);
tensor<int32, [2]> var_266 = const()[name = tensor<string, []>("op_266"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_268 = const()[name = tensor<string, []>("op_268"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_19_pad_type_0 = const()[name = tensor<string, []>("hidden_states_19_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_19_pad_0 = const()[name = tensor<string, []>("hidden_states_19_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<fp16, [512, 512, 3, 3]> encoder_down_blocks_2_resnets_1_conv2_weight_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_2_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18953536)))];
tensor<fp16, [512]> encoder_down_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23672192)))];
tensor<fp16, [1, 512, 96, 160]> 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);
tensor<fp16, [1, 512, 96, 160]> var_271_cast = add(x = var_241_cast, y = hidden_states_19_cast);
tensor<int32, [8]> hidden_states_23_pad_0 = const()[name = tensor<string, []>("hidden_states_23_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 1, 0, 1])];
tensor<string, []> hidden_states_23_mode_0 = const()[name = tensor<string, []>("hidden_states_23_mode_0"), val = tensor<string, []>("constant")];
tensor<fp16, []> hidden_states_23_constant_val_0_to_fp16 = const()[name = tensor<string, []>("hidden_states_23_constant_val_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
tensor<fp16, [1, 512, 97, 161]> 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);
tensor<int32, [2]> var_279 = const()[name = tensor<string, []>("op_279"), val = tensor<int32, [2]>([2, 2])];
tensor<int32, [2]> var_281 = const()[name = tensor<string, []>("op_281"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_85_pad_type_0 = const()[name = tensor<string, []>("input_85_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_85_pad_0 = const()[name = tensor<string, []>("input_85_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [512, 512, 3, 3]> encoder_down_blocks_2_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_2_downsamplers_0_conv_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23673280)))];
tensor<fp16, [512]> encoder_down_blocks_2_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_2_downsamplers_0_conv_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28391936)))];
tensor<fp16, [1, 512, 48, 80]> 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);
tensor<int32, [5]> reshape_48_shape_0 = const()[name = tensor<string, []>("reshape_48_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 48, 80])];
tensor<fp16, [1, 32, 16, 48, 80]> reshape_48_cast = reshape(shape = reshape_48_shape_0, x = input_85_cast);
tensor<int32, [3]> reduce_mean_36_axes_0 = const()[name = tensor<string, []>("reduce_mean_36_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_36_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_36_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_36_cast = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48_cast);
tensor<fp16, [1, 32, 16, 48, 80]> sub_24_cast = sub(x = reshape_48_cast, y = reduce_mean_36_cast);
tensor<fp16, [1, 32, 16, 48, 80]> square_12_cast = square(x = sub_24_cast);
tensor<int32, [3]> reduce_mean_38_axes_0 = const()[name = tensor<string, []>("reduce_mean_38_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_38_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_38_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_38_cast = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12_cast);
tensor<fp16, []> add_24_y_0_to_fp16 = const()[name = tensor<string, []>("add_24_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_24_cast = add(x = reduce_mean_38_cast, y = add_24_y_0_to_fp16);
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_12_cast = sqrt(x = add_24_cast);
tensor<fp16, [1, 32, 16, 48, 80]> real_div_12_cast = real_div(x = sub_24_cast, y = sqrt_12_cast);
tensor<int32, [4]> reshape_49_shape_0 = const()[name = tensor<string, []>("reshape_49_shape_0"), val = tensor<int32, [4]>([1, 512, 48, 80])];
tensor<fp16, [1, 512, 48, 80]> reshape_49_cast = reshape(shape = reshape_49_shape_0, x = real_div_12_cast);
tensor<fp16, [512]> add_25_mean_0_to_fp16 = const()[name = tensor<string, []>("add_25_mean_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28393024)))];
tensor<fp16, [512]> add_25_variance_0_to_fp16 = const()[name = tensor<string, []>("add_25_variance_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28394112)))];
tensor<fp16, [512]> add_25_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_25_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28395200)))];
tensor<fp16, [512]> add_25_beta_0_to_fp16 = const()[name = tensor<string, []>("add_25_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28396288)))];
tensor<fp16, []> add_25_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_25_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 48, 80]> 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_25_mean_0_to_fp16, variance = add_25_variance_0_to_fp16, x = reshape_49_cast);
tensor<fp16, [1, 512, 48, 80]> input_89_cast = silu(x = add_25_cast);
tensor<int32, [2]> var_298 = const()[name = tensor<string, []>("op_298"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_300 = const()[name = tensor<string, []>("op_300"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_91_pad_type_0 = const()[name = tensor<string, []>("input_91_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_91_pad_0 = const()[name = tensor<string, []>("input_91_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<fp16, [512, 512, 3, 3]> encoder_down_blocks_3_resnets_0_conv1_weight_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_3_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(28397376)))];
tensor<fp16, [512]> encoder_down_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33116032)))];
tensor<fp16, [1, 512, 48, 80]> 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);
tensor<int32, [5]> reshape_52_shape_0 = const()[name = tensor<string, []>("reshape_52_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 48, 80])];
tensor<fp16, [1, 32, 16, 48, 80]> reshape_52_cast = reshape(shape = reshape_52_shape_0, x = input_91_cast);
tensor<int32, [3]> reduce_mean_39_axes_0 = const()[name = tensor<string, []>("reduce_mean_39_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_39_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_39_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_39_cast = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52_cast);
tensor<fp16, [1, 32, 16, 48, 80]> sub_26_cast = sub(x = reshape_52_cast, y = reduce_mean_39_cast);
tensor<fp16, [1, 32, 16, 48, 80]> square_13_cast = square(x = sub_26_cast);
tensor<int32, [3]> reduce_mean_41_axes_0 = const()[name = tensor<string, []>("reduce_mean_41_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_41_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_41_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_41_cast = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13_cast);
tensor<fp16, []> add_26_y_0_to_fp16 = const()[name = tensor<string, []>("add_26_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_26_cast = add(x = reduce_mean_41_cast, y = add_26_y_0_to_fp16);
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_13_cast = sqrt(x = add_26_cast);
tensor<fp16, [1, 32, 16, 48, 80]> real_div_13_cast = real_div(x = sub_26_cast, y = sqrt_13_cast);
tensor<int32, [4]> reshape_53_shape_0 = const()[name = tensor<string, []>("reshape_53_shape_0"), val = tensor<int32, [4]>([1, 512, 48, 80])];
tensor<fp16, [1, 512, 48, 80]> reshape_53_cast = reshape(shape = reshape_53_shape_0, x = real_div_13_cast);
tensor<fp16, [512]> add_27_mean_0_to_fp16 = const()[name = tensor<string, []>("add_27_mean_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33117120)))];
tensor<fp16, [512]> add_27_variance_0_to_fp16 = const()[name = tensor<string, []>("add_27_variance_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33118208)))];
tensor<fp16, [512]> add_27_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_27_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33119296)))];
tensor<fp16, [512]> add_27_beta_0_to_fp16 = const()[name = tensor<string, []>("add_27_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33120384)))];
tensor<fp16, []> add_27_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_27_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 48, 80]> 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_53_cast);
tensor<fp16, [1, 512, 48, 80]> input_95_cast = silu(x = add_27_cast);
tensor<int32, [2]> var_310 = const()[name = tensor<string, []>("op_310"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_312 = const()[name = tensor<string, []>("op_312"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_25_pad_type_0 = const()[name = tensor<string, []>("hidden_states_25_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_25_pad_0 = const()[name = tensor<string, []>("hidden_states_25_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<fp16, [512, 512, 3, 3]> encoder_down_blocks_3_resnets_0_conv2_weight_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_3_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33121472)))];
tensor<fp16, [512]> encoder_down_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37840128)))];
tensor<fp16, [1, 512, 48, 80]> 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);
tensor<fp16, [1, 512, 48, 80]> var_315_cast = add(x = input_85_cast, y = hidden_states_25_cast);
tensor<int32, [5]> reshape_56_shape_0 = const()[name = tensor<string, []>("reshape_56_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 48, 80])];
tensor<fp16, [1, 32, 16, 48, 80]> reshape_56_cast = reshape(shape = reshape_56_shape_0, x = var_315_cast);
tensor<int32, [3]> reduce_mean_42_axes_0 = const()[name = tensor<string, []>("reduce_mean_42_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_42_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_42_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_42_cast = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56_cast);
tensor<fp16, [1, 32, 16, 48, 80]> sub_28_cast = sub(x = reshape_56_cast, y = reduce_mean_42_cast);
tensor<fp16, [1, 32, 16, 48, 80]> square_14_cast = square(x = sub_28_cast);
tensor<int32, [3]> reduce_mean_44_axes_0 = const()[name = tensor<string, []>("reduce_mean_44_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_44_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_44_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_44_cast = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14_cast);
tensor<fp16, []> add_28_y_0_to_fp16 = const()[name = tensor<string, []>("add_28_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_28_cast = add(x = reduce_mean_44_cast, y = add_28_y_0_to_fp16);
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_14_cast = sqrt(x = add_28_cast);
tensor<fp16, [1, 32, 16, 48, 80]> real_div_14_cast = real_div(x = sub_28_cast, y = sqrt_14_cast);
tensor<int32, [4]> reshape_57_shape_0 = const()[name = tensor<string, []>("reshape_57_shape_0"), val = tensor<int32, [4]>([1, 512, 48, 80])];
tensor<fp16, [1, 512, 48, 80]> reshape_57_cast = reshape(shape = reshape_57_shape_0, x = real_div_14_cast);
tensor<fp16, [512]> add_29_mean_0_to_fp16 = const()[name = tensor<string, []>("add_29_mean_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37841216)))];
tensor<fp16, [512]> add_29_variance_0_to_fp16 = const()[name = tensor<string, []>("add_29_variance_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37842304)))];
tensor<fp16, [512]> add_29_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_29_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37843392)))];
tensor<fp16, [512]> add_29_beta_0_to_fp16 = const()[name = tensor<string, []>("add_29_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37844480)))];
tensor<fp16, []> add_29_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_29_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 48, 80]> 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_29_mean_0_to_fp16, variance = add_29_variance_0_to_fp16, x = reshape_57_cast);
tensor<fp16, [1, 512, 48, 80]> input_103_cast = silu(x = add_29_cast);
tensor<int32, [2]> var_328 = const()[name = tensor<string, []>("op_328"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_330 = const()[name = tensor<string, []>("op_330"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_105_pad_type_0 = const()[name = tensor<string, []>("input_105_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_105_pad_0 = const()[name = tensor<string, []>("input_105_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<fp16, [512, 512, 3, 3]> encoder_down_blocks_3_resnets_1_conv1_weight_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_3_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37845568)))];
tensor<fp16, [512]> encoder_down_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42564224)))];
tensor<fp16, [1, 512, 48, 80]> 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);
tensor<int32, [5]> reshape_60_shape_0 = const()[name = tensor<string, []>("reshape_60_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 48, 80])];
tensor<fp16, [1, 32, 16, 48, 80]> reshape_60_cast = reshape(shape = reshape_60_shape_0, x = input_105_cast);
tensor<int32, [3]> reduce_mean_45_axes_0 = const()[name = tensor<string, []>("reduce_mean_45_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_45_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_45_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_45_cast = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60_cast);
tensor<fp16, [1, 32, 16, 48, 80]> sub_30_cast = sub(x = reshape_60_cast, y = reduce_mean_45_cast);
tensor<fp16, [1, 32, 16, 48, 80]> square_15_cast = square(x = sub_30_cast);
tensor<int32, [3]> reduce_mean_47_axes_0 = const()[name = tensor<string, []>("reduce_mean_47_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_47_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_47_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_47_cast = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15_cast);
tensor<fp16, []> add_30_y_0_to_fp16 = const()[name = tensor<string, []>("add_30_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_30_cast = add(x = reduce_mean_47_cast, y = add_30_y_0_to_fp16);
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_15_cast = sqrt(x = add_30_cast);
tensor<fp16, [1, 32, 16, 48, 80]> real_div_15_cast = real_div(x = sub_30_cast, y = sqrt_15_cast);
tensor<int32, [4]> reshape_61_shape_0 = const()[name = tensor<string, []>("reshape_61_shape_0"), val = tensor<int32, [4]>([1, 512, 48, 80])];
tensor<fp16, [1, 512, 48, 80]> reshape_61_cast = reshape(shape = reshape_61_shape_0, x = real_div_15_cast);
tensor<fp16, [512]> add_31_mean_0_to_fp16 = const()[name = tensor<string, []>("add_31_mean_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42565312)))];
tensor<fp16, [512]> add_31_variance_0_to_fp16 = const()[name = tensor<string, []>("add_31_variance_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42566400)))];
tensor<fp16, [512]> add_31_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_31_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42567488)))];
tensor<fp16, [512]> add_31_beta_0_to_fp16 = const()[name = tensor<string, []>("add_31_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42568576)))];
tensor<fp16, []> add_31_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_31_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 48, 80]> 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_31_mean_0_to_fp16, variance = add_31_variance_0_to_fp16, x = reshape_61_cast);
tensor<fp16, [1, 512, 48, 80]> input_109_cast = silu(x = add_31_cast);
tensor<int32, [2]> var_340 = const()[name = tensor<string, []>("op_340"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_342 = const()[name = tensor<string, []>("op_342"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_27_pad_type_0 = const()[name = tensor<string, []>("hidden_states_27_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_27_pad_0 = const()[name = tensor<string, []>("hidden_states_27_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<fp16, [512, 512, 3, 3]> encoder_down_blocks_3_resnets_1_conv2_weight_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_3_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(42569664)))];
tensor<fp16, [512]> encoder_down_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("encoder_down_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47288320)))];
tensor<fp16, [1, 512, 48, 80]> 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);
tensor<fp16, [1, 512, 48, 80]> var_345_cast = add(x = var_315_cast, y = hidden_states_27_cast);
tensor<int32, [5]> reshape_64_shape_0 = const()[name = tensor<string, []>("reshape_64_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 48, 80])];
tensor<fp16, [1, 32, 16, 48, 80]> reshape_64_cast = reshape(shape = reshape_64_shape_0, x = var_345_cast);
tensor<int32, [3]> reduce_mean_48_axes_0 = const()[name = tensor<string, []>("reduce_mean_48_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_48_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_48_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_48_cast = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64_cast);
tensor<fp16, [1, 32, 16, 48, 80]> sub_32_cast = sub(x = reshape_64_cast, y = reduce_mean_48_cast);
tensor<fp16, [1, 32, 16, 48, 80]> square_16_cast = square(x = sub_32_cast);
tensor<int32, [3]> reduce_mean_50_axes_0 = const()[name = tensor<string, []>("reduce_mean_50_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_50_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_50_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_50_cast = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16_cast);
tensor<fp16, []> add_32_y_0_to_fp16 = const()[name = tensor<string, []>("add_32_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_32_cast = add(x = reduce_mean_50_cast, y = add_32_y_0_to_fp16);
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_16_cast = sqrt(x = add_32_cast);
tensor<fp16, [1, 32, 16, 48, 80]> real_div_16_cast = real_div(x = sub_32_cast, y = sqrt_16_cast);
tensor<int32, [4]> reshape_65_shape_0 = const()[name = tensor<string, []>("reshape_65_shape_0"), val = tensor<int32, [4]>([1, 512, 48, 80])];
tensor<fp16, [1, 512, 48, 80]> reshape_65_cast = reshape(shape = reshape_65_shape_0, x = real_div_16_cast);
tensor<fp16, [512]> add_33_mean_0_to_fp16 = const()[name = tensor<string, []>("add_33_mean_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47289408)))];
tensor<fp16, [512]> add_33_variance_0_to_fp16 = const()[name = tensor<string, []>("add_33_variance_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47290496)))];
tensor<fp16, [512]> add_33_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_33_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47291584)))];
tensor<fp16, [512]> add_33_beta_0_to_fp16 = const()[name = tensor<string, []>("add_33_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47292672)))];
tensor<fp16, []> add_33_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_33_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 48, 80]> 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_33_mean_0_to_fp16, variance = add_33_variance_0_to_fp16, x = reshape_65_cast);
tensor<fp16, [1, 512, 48, 80]> input_117_cast = silu(x = add_33_cast);
tensor<int32, [2]> var_364 = const()[name = tensor<string, []>("op_364"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_366 = const()[name = tensor<string, []>("op_366"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_119_pad_type_0 = const()[name = tensor<string, []>("input_119_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_119_pad_0 = const()[name = tensor<string, []>("input_119_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<fp16, [512, 512, 3, 3]> encoder_mid_block_resnets_0_conv1_weight_to_fp16 = const()[name = tensor<string, []>("encoder_mid_block_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(47293760)))];
tensor<fp16, [512]> encoder_mid_block_resnets_0_conv1_bias_to_fp16 = const()[name = tensor<string, []>("encoder_mid_block_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(52012416)))];
tensor<fp16, [1, 512, 48, 80]> 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);
tensor<int32, [5]> reshape_68_shape_0 = const()[name = tensor<string, []>("reshape_68_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 48, 80])];
tensor<fp16, [1, 32, 16, 48, 80]> reshape_68_cast = reshape(shape = reshape_68_shape_0, x = input_119_cast);
tensor<int32, [3]> reduce_mean_51_axes_0 = const()[name = tensor<string, []>("reduce_mean_51_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_51_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_51_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_51_cast = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68_cast);
tensor<fp16, [1, 32, 16, 48, 80]> sub_34_cast = sub(x = reshape_68_cast, y = reduce_mean_51_cast);
tensor<fp16, [1, 32, 16, 48, 80]> square_17_cast = square(x = sub_34_cast);
tensor<int32, [3]> reduce_mean_53_axes_0 = const()[name = tensor<string, []>("reduce_mean_53_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_53_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_53_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_53_cast = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17_cast);
tensor<fp16, []> add_34_y_0_to_fp16 = const()[name = tensor<string, []>("add_34_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_34_cast = add(x = reduce_mean_53_cast, y = add_34_y_0_to_fp16);
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_17_cast = sqrt(x = add_34_cast);
tensor<fp16, [1, 32, 16, 48, 80]> real_div_17_cast = real_div(x = sub_34_cast, y = sqrt_17_cast);
tensor<int32, [4]> reshape_69_shape_0 = const()[name = tensor<string, []>("reshape_69_shape_0"), val = tensor<int32, [4]>([1, 512, 48, 80])];
tensor<fp16, [1, 512, 48, 80]> reshape_69_cast = reshape(shape = reshape_69_shape_0, x = real_div_17_cast);
tensor<fp16, [512]> add_35_mean_0_to_fp16 = const()[name = tensor<string, []>("add_35_mean_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(52013504)))];
tensor<fp16, [512]> add_35_variance_0_to_fp16 = const()[name = tensor<string, []>("add_35_variance_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(52014592)))];
tensor<fp16, [512]> add_35_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_35_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(52015680)))];
tensor<fp16, [512]> add_35_beta_0_to_fp16 = const()[name = tensor<string, []>("add_35_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(52016768)))];
tensor<fp16, []> add_35_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_35_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 48, 80]> 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_35_mean_0_to_fp16, variance = add_35_variance_0_to_fp16, x = reshape_69_cast);
tensor<fp16, [1, 512, 48, 80]> input_123_cast = silu(x = add_35_cast);
tensor<int32, [2]> var_376 = const()[name = tensor<string, []>("op_376"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_378 = const()[name = tensor<string, []>("op_378"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_29_pad_type_0 = const()[name = tensor<string, []>("hidden_states_29_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_29_pad_0 = const()[name = tensor<string, []>("hidden_states_29_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<fp16, [512, 512, 3, 3]> encoder_mid_block_resnets_0_conv2_weight_to_fp16 = const()[name = tensor<string, []>("encoder_mid_block_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(52017856)))];
tensor<fp16, [512]> encoder_mid_block_resnets_0_conv2_bias_to_fp16 = const()[name = tensor<string, []>("encoder_mid_block_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(56736512)))];
tensor<fp16, [1, 512, 48, 80]> 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);
tensor<fp16, [1, 512, 48, 80]> var_381_cast = add(x = var_345_cast, y = hidden_states_29_cast);
tensor<int32, [5]> reshape_72_shape_0 = const()[name = tensor<string, []>("reshape_72_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 48, 80])];
tensor<fp16, [1, 32, 16, 48, 80]> reshape_72_cast = reshape(shape = reshape_72_shape_0, x = var_381_cast);
tensor<int32, [3]> reduce_mean_54_axes_0 = const()[name = tensor<string, []>("reduce_mean_54_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_54_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_54_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_54_cast = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72_cast);
tensor<fp16, [1, 32, 16, 48, 80]> sub_36_cast = sub(x = reshape_72_cast, y = reduce_mean_54_cast);
tensor<fp16, [1, 32, 16, 48, 80]> square_18_cast = square(x = sub_36_cast);
tensor<int32, [3]> reduce_mean_56_axes_0 = const()[name = tensor<string, []>("reduce_mean_56_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_56_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_56_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_56_cast = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18_cast);
tensor<fp16, []> add_36_y_0_to_fp16 = const()[name = tensor<string, []>("add_36_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_36_cast = add(x = reduce_mean_56_cast, y = add_36_y_0_to_fp16);
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_18_cast = sqrt(x = add_36_cast);
tensor<fp16, [1, 32, 16, 48, 80]> real_div_18_cast = real_div(x = sub_36_cast, y = sqrt_18_cast);
tensor<int32, [4]> reshape_73_shape_0 = const()[name = tensor<string, []>("reshape_73_shape_0"), val = tensor<int32, [4]>([1, 512, 48, 80])];
tensor<fp16, [1, 512, 48, 80]> reshape_73_cast = reshape(shape = reshape_73_shape_0, x = real_div_18_cast);
tensor<fp16, [512]> add_37_mean_0_to_fp16 = const()[name = tensor<string, []>("add_37_mean_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(56737600)))];
tensor<fp16, [512]> add_37_variance_0_to_fp16 = const()[name = tensor<string, []>("add_37_variance_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(56738688)))];
tensor<fp16, [512]> add_37_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_37_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(56739776)))];
tensor<fp16, [512]> add_37_beta_0_to_fp16 = const()[name = tensor<string, []>("add_37_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(56740864)))];
tensor<fp16, []> add_37_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_37_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 48, 80]> 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_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_73_cast);
tensor<int32, [3]> var_400 = const()[name = tensor<string, []>("op_400"), val = tensor<int32, [3]>([1, 512, 3840])];
tensor<fp16, [1, 512, 3840]> var_401_cast = reshape(shape = var_400, x = add_37_cast);
tensor<int32, [3]> input_127_perm_0 = const()[name = tensor<string, []>("input_127_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<fp16, [512, 512]> encoder_mid_block_attentions_0_query_weight_to_fp16 = const()[name = tensor<string, []>("encoder_mid_block_attentions_0_query_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(56741952)))];
tensor<fp16, [512]> encoder_mid_block_attentions_0_query_bias_to_fp16 = const()[name = tensor<string, []>("encoder_mid_block_attentions_0_query_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(57266304)))];
tensor<fp16, [1, 3840, 512]> transpose_6 = transpose(perm = input_127_perm_0, x = var_401_cast);
tensor<fp16, [1, 3840, 512]> 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);
tensor<fp16, [512, 512]> encoder_mid_block_attentions_0_key_weight_to_fp16 = const()[name = tensor<string, []>("encoder_mid_block_attentions_0_key_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(57267392)))];
tensor<fp16, [512]> encoder_mid_block_attentions_0_key_bias_to_fp16 = const()[name = tensor<string, []>("encoder_mid_block_attentions_0_key_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(57791744)))];
tensor<fp16, [1, 3840, 512]> tensor_5_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);
tensor<fp16, [512, 512]> encoder_mid_block_attentions_0_value_weight_to_fp16 = const()[name = tensor<string, []>("encoder_mid_block_attentions_0_value_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(57792832)))];
tensor<fp16, [512]> encoder_mid_block_attentions_0_value_bias_to_fp16 = const()[name = tensor<string, []>("encoder_mid_block_attentions_0_value_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(58317184)))];
tensor<fp16, [1, 3840, 512]> tensor_9_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);
tensor<int32, [4]> var_419 = const()[name = tensor<string, []>("op_419"), val = tensor<int32, [4]>([1, 3840, 1, 512])];
tensor<fp16, [1, 3840, 1, 512]> tensor_3_cast = reshape(shape = var_419, x = tensor_1_cast);
tensor<int32, [4]> var_421 = const()[name = tensor<string, []>("op_421"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_428 = const()[name = tensor<string, []>("op_428"), val = tensor<int32, [3]>([1, 3840, 512])];
tensor<fp16, [1, 1, 3840, 512]> transpose_5 = transpose(perm = var_421, x = tensor_3_cast);
tensor<fp16, [1, 3840, 512]> query_proj_cast = reshape(shape = var_428, x = transpose_5);
tensor<int32, [4]> var_437 = const()[name = tensor<string, []>("op_437"), val = tensor<int32, [4]>([1, 3840, 1, 512])];
tensor<fp16, [1, 3840, 1, 512]> tensor_7_cast = reshape(shape = var_437, x = tensor_5_cast);
tensor<int32, [4]> var_439 = const()[name = tensor<string, []>("op_439"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_446 = const()[name = tensor<string, []>("op_446"), val = tensor<int32, [3]>([1, 3840, 512])];
tensor<fp16, [1, 1, 3840, 512]> transpose_4 = transpose(perm = var_439, x = tensor_7_cast);
tensor<fp16, [1, 3840, 512]> key_proj_cast = reshape(shape = var_446, x = transpose_4);
tensor<int32, [4]> var_455 = const()[name = tensor<string, []>("op_455"), val = tensor<int32, [4]>([1, 3840, 1, 512])];
tensor<fp16, [1, 3840, 1, 512]> tensor_11_cast = reshape(shape = var_455, x = tensor_9_cast);
tensor<int32, [4]> var_457 = const()[name = tensor<string, []>("op_457"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_464 = const()[name = tensor<string, []>("op_464"), val = tensor<int32, [3]>([1, 3840, 512])];
tensor<fp16, [1, 1, 3840, 512]> transpose_3 = transpose(perm = var_457, x = tensor_11_cast);
tensor<fp16, [1, 3840, 512]> value_proj_cast = reshape(shape = var_464, x = transpose_3);
tensor<int32, [3]> var_471_perm_0 = const()[name = tensor<string, []>("op_471_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
tensor<fp16, []> var_5_to_fp16 = const()[name = tensor<string, []>("op_5_to_fp16"), val = tensor<fp16, []>(0x1.6ap-5)];
tensor<fp16, [1, 3840, 512]> query_proj_scaled_cast = mul(x = var_5_to_fp16, y = query_proj_cast);
tensor<bool, []> attention_scores_1_bmm_transpose_x_0 = const()[name = tensor<string, []>("attention_scores_1_bmm_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> attention_scores_1_bmm_transpose_y_0 = const()[name = tensor<string, []>("attention_scores_1_bmm_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 512, 3840]> transpose_2 = transpose(perm = var_471_perm_0, x = key_proj_cast);
tensor<fp16, [1, 3840, 3840]> 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);
tensor<fp16, [1, 3840, 3840]> var_474_cast = softmax(axis = var_7, x = attention_scores_1_bmm_cast);
tensor<bool, []> tensor_13_transpose_x_0 = const()[name = tensor<string, []>("tensor_13_transpose_x_0"), val = tensor<bool, []>(false)];
tensor<bool, []> tensor_13_transpose_y_0 = const()[name = tensor<string, []>("tensor_13_transpose_y_0"), val = tensor<bool, []>(false)];
tensor<fp16, [1, 3840, 512]> tensor_13_cast = matmul(transpose_x = tensor_13_transpose_x_0, transpose_y = tensor_13_transpose_y_0, x = var_474_cast, y = value_proj_cast);
tensor<int32, [4]> var_484 = const()[name = tensor<string, []>("op_484"), val = tensor<int32, [4]>([1, 1, 3840, 512])];
tensor<fp16, [1, 1, 3840, 512]> tensor_cast = reshape(shape = var_484, x = tensor_13_cast);
tensor<int32, [4]> var_486 = const()[name = tensor<string, []>("op_486"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_493 = const()[name = tensor<string, []>("op_493"), val = tensor<int32, [3]>([1, 3840, 512])];
tensor<fp16, [1, 3840, 1, 512]> transpose_1 = transpose(perm = var_486, x = tensor_cast);
tensor<fp16, [1, 3840, 512]> input_129_cast = reshape(shape = var_493, x = transpose_1);
tensor<fp16, [512, 512]> encoder_mid_block_attentions_0_proj_attn_weight_to_fp16 = const()[name = tensor<string, []>("encoder_mid_block_attentions_0_proj_attn_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(58318272)))];
tensor<fp16, [512]> encoder_mid_block_attentions_0_proj_attn_bias_to_fp16 = const()[name = tensor<string, []>("encoder_mid_block_attentions_0_proj_attn_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(58842624)))];
tensor<fp16, [1, 3840, 512]> 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);
tensor<int32, [3]> var_498_perm_0 = const()[name = tensor<string, []>("op_498_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
tensor<int32, [4]> var_499 = const()[name = tensor<string, []>("op_499"), val = tensor<int32, [4]>([1, 512, 48, 80])];
tensor<fp16, [1, 512, 3840]> transpose_0 = transpose(perm = var_498_perm_0, x = hidden_states_35_cast);
tensor<fp16, [1, 512, 48, 80]> hidden_states_37_cast = reshape(shape = var_499, x = transpose_0);
tensor<fp16, [1, 512, 48, 80]> var_501_cast = add(x = hidden_states_37_cast, y = var_381_cast);
tensor<int32, [5]> reshape_76_shape_0 = const()[name = tensor<string, []>("reshape_76_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 48, 80])];
tensor<fp16, [1, 32, 16, 48, 80]> reshape_76_cast = reshape(shape = reshape_76_shape_0, x = var_501_cast);
tensor<int32, [3]> reduce_mean_57_axes_0 = const()[name = tensor<string, []>("reduce_mean_57_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_57_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_57_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_57_cast = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76_cast);
tensor<fp16, [1, 32, 16, 48, 80]> sub_38_cast = sub(x = reshape_76_cast, y = reduce_mean_57_cast);
tensor<fp16, [1, 32, 16, 48, 80]> square_19_cast = square(x = sub_38_cast);
tensor<int32, [3]> reduce_mean_59_axes_0 = const()[name = tensor<string, []>("reduce_mean_59_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_59_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_59_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_59_cast = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19_cast);
tensor<fp16, []> add_38_y_0_to_fp16 = const()[name = tensor<string, []>("add_38_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_38_cast = add(x = reduce_mean_59_cast, y = add_38_y_0_to_fp16);
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_19_cast = sqrt(x = add_38_cast);
tensor<fp16, [1, 32, 16, 48, 80]> real_div_19_cast = real_div(x = sub_38_cast, y = sqrt_19_cast);
tensor<int32, [4]> reshape_77_shape_0 = const()[name = tensor<string, []>("reshape_77_shape_0"), val = tensor<int32, [4]>([1, 512, 48, 80])];
tensor<fp16, [1, 512, 48, 80]> reshape_77_cast = reshape(shape = reshape_77_shape_0, x = real_div_19_cast);
tensor<fp16, [512]> add_39_mean_0_to_fp16 = const()[name = tensor<string, []>("add_39_mean_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(58843712)))];
tensor<fp16, [512]> add_39_variance_0_to_fp16 = const()[name = tensor<string, []>("add_39_variance_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(58844800)))];
tensor<fp16, [512]> add_39_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_39_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(58845888)))];
tensor<fp16, [512]> add_39_beta_0_to_fp16 = const()[name = tensor<string, []>("add_39_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(58846976)))];
tensor<fp16, []> add_39_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_39_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 48, 80]> 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_39_mean_0_to_fp16, variance = add_39_variance_0_to_fp16, x = reshape_77_cast);
tensor<fp16, [1, 512, 48, 80]> input_135_cast = silu(x = add_39_cast);
tensor<int32, [2]> var_514 = const()[name = tensor<string, []>("op_514"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_516 = const()[name = tensor<string, []>("op_516"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_137_pad_type_0 = const()[name = tensor<string, []>("input_137_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_137_pad_0 = const()[name = tensor<string, []>("input_137_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<fp16, [512, 512, 3, 3]> encoder_mid_block_resnets_1_conv1_weight_to_fp16 = const()[name = tensor<string, []>("encoder_mid_block_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(58848064)))];
tensor<fp16, [512]> encoder_mid_block_resnets_1_conv1_bias_to_fp16 = const()[name = tensor<string, []>("encoder_mid_block_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63566720)))];
tensor<fp16, [1, 512, 48, 80]> input_137_cast = conv(bias = encoder_mid_block_resnets_1_conv1_bias_to_fp16, dilations = var_516, groups = var_17, pad = input_137_pad_0, pad_type = input_137_pad_type_0, strides = var_514, weight = encoder_mid_block_resnets_1_conv1_weight_to_fp16, x = input_135_cast);
tensor<int32, [5]> reshape_80_shape_0 = const()[name = tensor<string, []>("reshape_80_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 48, 80])];
tensor<fp16, [1, 32, 16, 48, 80]> reshape_80_cast = reshape(shape = reshape_80_shape_0, x = input_137_cast);
tensor<int32, [3]> reduce_mean_60_axes_0 = const()[name = tensor<string, []>("reduce_mean_60_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_60_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_60_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_60_cast = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80_cast);
tensor<fp16, [1, 32, 16, 48, 80]> sub_40_cast = sub(x = reshape_80_cast, y = reduce_mean_60_cast);
tensor<fp16, [1, 32, 16, 48, 80]> square_20_cast = square(x = sub_40_cast);
tensor<int32, [3]> reduce_mean_62_axes_0 = const()[name = tensor<string, []>("reduce_mean_62_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_62_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_62_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_62_cast = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20_cast);
tensor<fp16, []> add_40_y_0_to_fp16 = const()[name = tensor<string, []>("add_40_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_40_cast = add(x = reduce_mean_62_cast, y = add_40_y_0_to_fp16);
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_20_cast = sqrt(x = add_40_cast);
tensor<fp16, [1, 32, 16, 48, 80]> real_div_20_cast = real_div(x = sub_40_cast, y = sqrt_20_cast);
tensor<int32, [4]> reshape_81_shape_0 = const()[name = tensor<string, []>("reshape_81_shape_0"), val = tensor<int32, [4]>([1, 512, 48, 80])];
tensor<fp16, [1, 512, 48, 80]> reshape_81_cast = reshape(shape = reshape_81_shape_0, x = real_div_20_cast);
tensor<fp16, [512]> add_41_mean_0_to_fp16 = const()[name = tensor<string, []>("add_41_mean_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63567808)))];
tensor<fp16, [512]> add_41_variance_0_to_fp16 = const()[name = tensor<string, []>("add_41_variance_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63568896)))];
tensor<fp16, [512]> add_41_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_41_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63569984)))];
tensor<fp16, [512]> add_41_beta_0_to_fp16 = const()[name = tensor<string, []>("add_41_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63571072)))];
tensor<fp16, []> add_41_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_41_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 48, 80]> 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_41_mean_0_to_fp16, variance = add_41_variance_0_to_fp16, x = reshape_81_cast);
tensor<fp16, [1, 512, 48, 80]> input_141_cast = silu(x = add_41_cast);
tensor<int32, [2]> var_526 = const()[name = tensor<string, []>("op_526"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_528 = const()[name = tensor<string, []>("op_528"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> hidden_states_pad_type_0 = const()[name = tensor<string, []>("hidden_states_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> hidden_states_pad_0 = const()[name = tensor<string, []>("hidden_states_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<fp16, [512, 512, 3, 3]> encoder_mid_block_resnets_1_conv2_weight_to_fp16 = const()[name = tensor<string, []>("encoder_mid_block_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63572160)))];
tensor<fp16, [512]> encoder_mid_block_resnets_1_conv2_bias_to_fp16 = const()[name = tensor<string, []>("encoder_mid_block_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68290816)))];
tensor<fp16, [1, 512, 48, 80]> hidden_states_cast = conv(bias = encoder_mid_block_resnets_1_conv2_bias_to_fp16, dilations = var_528, groups = var_17, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_526, weight = encoder_mid_block_resnets_1_conv2_weight_to_fp16, x = input_141_cast);
tensor<fp16, [1, 512, 48, 80]> var_531_cast = add(x = var_501_cast, y = hidden_states_cast);
tensor<int32, [5]> reshape_84_shape_0 = const()[name = tensor<string, []>("reshape_84_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 48, 80])];
tensor<fp16, [1, 32, 16, 48, 80]> reshape_84_cast = reshape(shape = reshape_84_shape_0, x = var_531_cast);
tensor<int32, [3]> reduce_mean_63_axes_0 = const()[name = tensor<string, []>("reduce_mean_63_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_63_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_63_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_63_cast = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84_cast);
tensor<fp16, [1, 32, 16, 48, 80]> sub_42_cast = sub(x = reshape_84_cast, y = reduce_mean_63_cast);
tensor<fp16, [1, 32, 16, 48, 80]> square_21_cast = square(x = sub_42_cast);
tensor<int32, [3]> reduce_mean_65_axes_0 = const()[name = tensor<string, []>("reduce_mean_65_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
tensor<bool, []> reduce_mean_65_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_65_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_65_cast = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21_cast);
tensor<fp16, []> add_42_y_0_to_fp16 = const()[name = tensor<string, []>("add_42_y_0_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_42_cast = add(x = reduce_mean_65_cast, y = add_42_y_0_to_fp16);
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_21_cast = sqrt(x = add_42_cast);
tensor<fp16, [1, 32, 16, 48, 80]> real_div_21_cast = real_div(x = sub_42_cast, y = sqrt_21_cast);
tensor<int32, [4]> reshape_85_shape_0 = const()[name = tensor<string, []>("reshape_85_shape_0"), val = tensor<int32, [4]>([1, 512, 48, 80])];
tensor<fp16, [1, 512, 48, 80]> reshape_85_cast = reshape(shape = reshape_85_shape_0, x = real_div_21_cast);
tensor<fp16, [512]> add_43_mean_0_to_fp16 = const()[name = tensor<string, []>("add_43_mean_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68291904)))];
tensor<fp16, [512]> add_43_variance_0_to_fp16 = const()[name = tensor<string, []>("add_43_variance_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68292992)))];
tensor<fp16, [512]> add_43_gamma_0_to_fp16 = const()[name = tensor<string, []>("add_43_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68294080)))];
tensor<fp16, [512]> add_43_beta_0_to_fp16 = const()[name = tensor<string, []>("add_43_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68295168)))];
tensor<fp16, []> add_43_epsilon_0_to_fp16 = const()[name = tensor<string, []>("add_43_epsilon_0_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 512, 48, 80]> 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_43_mean_0_to_fp16, variance = add_43_variance_0_to_fp16, x = reshape_85_cast);
tensor<fp16, [1, 512, 48, 80]> input_149_cast = silu(x = add_43_cast);
tensor<int32, [2]> var_540 = const()[name = tensor<string, []>("op_540"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_542 = const()[name = tensor<string, []>("op_542"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> input_pad_type_0 = const()[name = tensor<string, []>("input_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> input_pad_0 = const()[name = tensor<string, []>("input_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<fp16, [8, 512, 3, 3]> encoder_conv_out_weight_to_fp16 = const()[name = tensor<string, []>("encoder_conv_out_weight_to_fp16"), val = tensor<fp16, [8, 512, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68296256)))];
tensor<fp16, [8]> encoder_conv_out_bias_to_fp16 = const()[name = tensor<string, []>("encoder_conv_out_bias_to_fp16"), val = tensor<fp16, [8]>([-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<fp16, [1, 8, 48, 80]> input_cast = conv(bias = encoder_conv_out_bias_to_fp16, dilations = var_542, groups = var_17, pad = input_pad_0, pad_type = input_pad_type_0, strides = var_540, weight = encoder_conv_out_weight_to_fp16, x = input_149_cast);
tensor<int32, []> var_548 = const()[name = tensor<string, []>("op_548"), val = tensor<int32, []>(1)];
tensor<int32, [2]> var_551 = const()[name = tensor<string, []>("op_551"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> var_553 = const()[name = tensor<string, []>("op_553"), val = tensor<int32, [2]>([1, 1])];
tensor<string, []> var_555_pad_type_0 = const()[name = tensor<string, []>("op_555_pad_type_0"), val = tensor<string, []>("custom")];
tensor<int32, [4]> var_555_pad_0 = const()[name = tensor<string, []>("op_555_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<fp16, [8, 8, 1, 1]> quant_conv_weight_to_fp16 = const()[name = tensor<string, []>("quant_conv_weight_to_fp16"), val = tensor<fp16, [8, 8, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(68370048)))];
tensor<fp16, [8]> quant_conv_bias_to_fp16 = const()[name = tensor<string, []>("quant_conv_bias_to_fp16"), val = tensor<fp16, [8]>([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<fp16, [1, 8, 48, 80]> var_555_cast = conv(bias = quant_conv_bias_to_fp16, dilations = var_553, groups = var_548, pad = var_555_pad_0, pad_type = var_555_pad_type_0, strides = var_551, weight = quant_conv_weight_to_fp16, x = input_cast);
tensor<string, []> var_555_cast_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_555_cast_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
tensor<fp32, [1, 8, 48, 80]> latent = cast(dtype = var_555_cast_to_fp32_dtype_0, x = var_555_cast);
} -> (latent);
} |