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[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.22.1"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
{
func main<ios18>(tensor<fp16, [1, 3, 512, 512]> x) {
string input_1_pad_type_0 = const()[name = string("input_1_pad_type_0"), val = string("custom")];
tensor<int32, [4]> input_1_pad_0 = const()[name = string("input_1_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<int32, [2]> input_1_strides_0 = const()[name = string("input_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> input_1_dilations_0 = const()[name = string("input_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 input_1_groups_0 = const()[name = string("input_1_groups_0"), val = int32(1)];
tensor<fp16, [128, 3, 3, 3]> encoder_conv_in_weight_to_fp16 = const()[name = string("encoder_conv_in_weight_to_fp16"), val = tensor<fp16, [128, 3, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
tensor<fp16, [128]> encoder_conv_in_bias_to_fp16 = const()[name = string("encoder_conv_in_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7040)))];
tensor<fp16, [1, 128, 512, 512]> input_1_cast_fp16 = conv(bias = encoder_conv_in_bias_to_fp16, dilations = input_1_dilations_0, groups = input_1_groups_0, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = input_1_strides_0, weight = encoder_conv_in_weight_to_fp16, x = x)[name = string("input_1_cast_fp16")];
tensor<int32, [5]> reshape_0_shape_0 = const()[name = string("reshape_0_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 512, 512])];
tensor<fp16, [1, 32, 4, 512, 512]> reshape_0_cast_fp16 = reshape(shape = reshape_0_shape_0, x = input_1_cast_fp16)[name = string("reshape_0_cast_fp16")];
tensor<int32, [3]> reduce_mean_0_axes_0 = const()[name = string("reduce_mean_0_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_0_keep_dims_0 = const()[name = string("reduce_mean_0_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_0_cast_fp16 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0_cast_fp16)[name = string("reduce_mean_0_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 512]> sub_0_cast_fp16 = sub(x = reshape_0_cast_fp16, y = reduce_mean_0_cast_fp16)[name = string("sub_0_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 512]> square_0_cast_fp16 = square(x = sub_0_cast_fp16)[name = string("square_0_cast_fp16")];
tensor<int32, [3]> reduce_mean_2_axes_0 = const()[name = string("reduce_mean_2_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_2_keep_dims_0 = const()[name = string("reduce_mean_2_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_2_cast_fp16 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0_cast_fp16)[name = string("reduce_mean_2_cast_fp16")];
fp16 add_0_y_0_to_fp16 = const()[name = string("add_0_y_0_to_fp16"), val = fp16(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_0_cast_fp16 = add(x = reduce_mean_2_cast_fp16, y = add_0_y_0_to_fp16)[name = string("add_0_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_0_cast_fp16 = sqrt(x = add_0_cast_fp16)[name = string("sqrt_0_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 512]> real_div_0_cast_fp16 = real_div(x = sub_0_cast_fp16, y = sqrt_0_cast_fp16)[name = string("real_div_0_cast_fp16")];
tensor<int32, [4]> reshape_1_shape_0 = const()[name = string("reshape_1_shape_0"), val = tensor<int32, [4]>([1, 128, 512, 512])];
tensor<fp16, [1, 128, 512, 512]> reshape_1_cast_fp16 = reshape(shape = reshape_1_shape_0, x = real_div_0_cast_fp16)[name = string("reshape_1_cast_fp16")];
tensor<fp16, [128]> add_1_mean_0_to_fp16 = const()[name = string("add_1_mean_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7360)))];
tensor<fp16, [128]> add_1_variance_0_to_fp16 = const()[name = string("add_1_variance_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7680)))];
tensor<fp16, [128]> add_1_gamma_0_to_fp16 = const()[name = string("add_1_gamma_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8000)))];
tensor<fp16, [128]> add_1_beta_0_to_fp16 = const()[name = string("add_1_beta_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8320)))];
fp16 add_1_epsilon_0_to_fp16 = const()[name = string("add_1_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, 128, 512, 512]> add_1_cast_fp16 = 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_fp16)[name = string("add_1_cast_fp16")];
tensor<fp16, [1, 128, 512, 512]> input_5_cast_fp16 = silu(x = add_1_cast_fp16)[name = string("input_5_cast_fp16")];
string input_7_pad_type_0 = const()[name = string("input_7_pad_type_0"), val = string("custom")];
tensor<int32, [4]> input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<int32, [2]> input_7_strides_0 = const()[name = string("input_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> input_7_dilations_0 = const()[name = string("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 input_7_groups_0 = const()[name = string("input_7_groups_0"), val = int32(1)];
tensor<fp16, [128, 128, 3, 3]> encoder_down_blocks_0_resnets_0_conv1_weight_to_fp16 = const()[name = string("encoder_down_blocks_0_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8640)))];
tensor<fp16, [128]> encoder_down_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = string("encoder_down_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303616)))];
tensor<fp16, [1, 128, 512, 512]> input_7_cast_fp16 = conv(bias = encoder_down_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = encoder_down_blocks_0_resnets_0_conv1_weight_to_fp16, x = input_5_cast_fp16)[name = string("input_7_cast_fp16")];
tensor<int32, [5]> reshape_4_shape_0 = const()[name = string("reshape_4_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 512, 512])];
tensor<fp16, [1, 32, 4, 512, 512]> reshape_4_cast_fp16 = reshape(shape = reshape_4_shape_0, x = input_7_cast_fp16)[name = string("reshape_4_cast_fp16")];
tensor<int32, [3]> reduce_mean_3_axes_0 = const()[name = string("reduce_mean_3_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_3_keep_dims_0 = const()[name = string("reduce_mean_3_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_3_cast_fp16 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4_cast_fp16)[name = string("reduce_mean_3_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 512]> sub_2_cast_fp16 = sub(x = reshape_4_cast_fp16, y = reduce_mean_3_cast_fp16)[name = string("sub_2_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 512]> square_1_cast_fp16 = square(x = sub_2_cast_fp16)[name = string("square_1_cast_fp16")];
tensor<int32, [3]> reduce_mean_5_axes_0 = const()[name = string("reduce_mean_5_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_5_keep_dims_0 = const()[name = string("reduce_mean_5_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_5_cast_fp16 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1_cast_fp16)[name = string("reduce_mean_5_cast_fp16")];
fp16 add_2_y_0_to_fp16 = const()[name = string("add_2_y_0_to_fp16"), val = fp16(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_2_cast_fp16 = add(x = reduce_mean_5_cast_fp16, y = add_2_y_0_to_fp16)[name = string("add_2_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_1_cast_fp16 = sqrt(x = add_2_cast_fp16)[name = string("sqrt_1_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 512]> real_div_1_cast_fp16 = real_div(x = sub_2_cast_fp16, y = sqrt_1_cast_fp16)[name = string("real_div_1_cast_fp16")];
tensor<int32, [4]> reshape_5_shape_0 = const()[name = string("reshape_5_shape_0"), val = tensor<int32, [4]>([1, 128, 512, 512])];
tensor<fp16, [1, 128, 512, 512]> reshape_5_cast_fp16 = reshape(shape = reshape_5_shape_0, x = real_div_1_cast_fp16)[name = string("reshape_5_cast_fp16")];
tensor<fp16, [128]> add_3_gamma_0_to_fp16 = const()[name = string("add_3_gamma_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303936)))];
tensor<fp16, [128]> add_3_beta_0_to_fp16 = const()[name = string("add_3_beta_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304256)))];
fp16 add_3_epsilon_0_to_fp16 = const()[name = string("add_3_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, 128, 512, 512]> add_3_cast_fp16 = batch_norm(beta = add_3_beta_0_to_fp16, epsilon = add_3_epsilon_0_to_fp16, gamma = add_3_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_5_cast_fp16)[name = string("add_3_cast_fp16")];
tensor<fp16, [1, 128, 512, 512]> input_11_cast_fp16 = silu(x = add_3_cast_fp16)[name = string("input_11_cast_fp16")];
string hidden_states_1_pad_type_0 = const()[name = string("hidden_states_1_pad_type_0"), val = string("custom")];
tensor<int32, [4]> hidden_states_1_pad_0 = const()[name = string("hidden_states_1_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<int32, [2]> hidden_states_1_strides_0 = const()[name = string("hidden_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> hidden_states_1_dilations_0 = const()[name = string("hidden_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 hidden_states_1_groups_0 = const()[name = string("hidden_states_1_groups_0"), val = int32(1)];
tensor<fp16, [128, 128, 3, 3]> encoder_down_blocks_0_resnets_0_conv2_weight_to_fp16 = const()[name = string("encoder_down_blocks_0_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(304576)))];
tensor<fp16, [128]> encoder_down_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = string("encoder_down_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(599552)))];
tensor<fp16, [1, 128, 512, 512]> hidden_states_1_cast_fp16 = conv(bias = encoder_down_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = hidden_states_1_dilations_0, groups = hidden_states_1_groups_0, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = hidden_states_1_strides_0, weight = encoder_down_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_11_cast_fp16)[name = string("hidden_states_1_cast_fp16")];
tensor<fp16, [1, 128, 512, 512]> var_71_cast_fp16 = add(x = input_1_cast_fp16, y = hidden_states_1_cast_fp16)[name = string("op_71_cast_fp16")];
tensor<int32, [5]> reshape_8_shape_0 = const()[name = string("reshape_8_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 512, 512])];
tensor<fp16, [1, 32, 4, 512, 512]> reshape_8_cast_fp16 = reshape(shape = reshape_8_shape_0, x = var_71_cast_fp16)[name = string("reshape_8_cast_fp16")];
tensor<int32, [3]> reduce_mean_6_axes_0 = const()[name = string("reduce_mean_6_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_6_keep_dims_0 = const()[name = string("reduce_mean_6_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_6_cast_fp16 = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8_cast_fp16)[name = string("reduce_mean_6_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 512]> sub_4_cast_fp16 = sub(x = reshape_8_cast_fp16, y = reduce_mean_6_cast_fp16)[name = string("sub_4_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 512]> square_2_cast_fp16 = square(x = sub_4_cast_fp16)[name = string("square_2_cast_fp16")];
tensor<int32, [3]> reduce_mean_8_axes_0 = const()[name = string("reduce_mean_8_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_8_keep_dims_0 = const()[name = string("reduce_mean_8_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_8_cast_fp16 = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2_cast_fp16)[name = string("reduce_mean_8_cast_fp16")];
fp16 add_4_y_0_to_fp16 = const()[name = string("add_4_y_0_to_fp16"), val = fp16(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_4_cast_fp16 = add(x = reduce_mean_8_cast_fp16, y = add_4_y_0_to_fp16)[name = string("add_4_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_2_cast_fp16 = sqrt(x = add_4_cast_fp16)[name = string("sqrt_2_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 512]> real_div_2_cast_fp16 = real_div(x = sub_4_cast_fp16, y = sqrt_2_cast_fp16)[name = string("real_div_2_cast_fp16")];
tensor<int32, [4]> reshape_9_shape_0 = const()[name = string("reshape_9_shape_0"), val = tensor<int32, [4]>([1, 128, 512, 512])];
tensor<fp16, [1, 128, 512, 512]> reshape_9_cast_fp16 = reshape(shape = reshape_9_shape_0, x = real_div_2_cast_fp16)[name = string("reshape_9_cast_fp16")];
tensor<fp16, [128]> add_5_gamma_0_to_fp16 = const()[name = string("add_5_gamma_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(599872)))];
tensor<fp16, [128]> add_5_beta_0_to_fp16 = const()[name = string("add_5_beta_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(600192)))];
fp16 add_5_epsilon_0_to_fp16 = const()[name = string("add_5_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, 128, 512, 512]> add_5_cast_fp16 = batch_norm(beta = add_5_beta_0_to_fp16, epsilon = add_5_epsilon_0_to_fp16, gamma = add_5_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_9_cast_fp16)[name = string("add_5_cast_fp16")];
tensor<fp16, [1, 128, 512, 512]> input_19_cast_fp16 = silu(x = add_5_cast_fp16)[name = string("input_19_cast_fp16")];
string input_21_pad_type_0 = const()[name = string("input_21_pad_type_0"), val = string("custom")];
tensor<int32, [4]> input_21_pad_0 = const()[name = string("input_21_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<int32, [2]> input_21_strides_0 = const()[name = string("input_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> input_21_dilations_0 = const()[name = string("input_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 input_21_groups_0 = const()[name = string("input_21_groups_0"), val = int32(1)];
tensor<fp16, [128, 128, 3, 3]> encoder_down_blocks_0_resnets_1_conv1_weight_to_fp16 = const()[name = string("encoder_down_blocks_0_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(600512)))];
tensor<fp16, [128]> encoder_down_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = string("encoder_down_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(895488)))];
tensor<fp16, [1, 128, 512, 512]> input_21_cast_fp16 = conv(bias = encoder_down_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = encoder_down_blocks_0_resnets_1_conv1_weight_to_fp16, x = input_19_cast_fp16)[name = string("input_21_cast_fp16")];
tensor<int32, [5]> reshape_12_shape_0 = const()[name = string("reshape_12_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 512, 512])];
tensor<fp16, [1, 32, 4, 512, 512]> reshape_12_cast_fp16 = reshape(shape = reshape_12_shape_0, x = input_21_cast_fp16)[name = string("reshape_12_cast_fp16")];
tensor<int32, [3]> reduce_mean_9_axes_0 = const()[name = string("reduce_mean_9_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_9_keep_dims_0 = const()[name = string("reduce_mean_9_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_9_cast_fp16 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12_cast_fp16)[name = string("reduce_mean_9_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 512]> sub_6_cast_fp16 = sub(x = reshape_12_cast_fp16, y = reduce_mean_9_cast_fp16)[name = string("sub_6_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 512]> square_3_cast_fp16 = square(x = sub_6_cast_fp16)[name = string("square_3_cast_fp16")];
tensor<int32, [3]> reduce_mean_11_axes_0 = const()[name = string("reduce_mean_11_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_11_keep_dims_0 = const()[name = string("reduce_mean_11_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_11_cast_fp16 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3_cast_fp16)[name = string("reduce_mean_11_cast_fp16")];
fp16 add_6_y_0_to_fp16 = const()[name = string("add_6_y_0_to_fp16"), val = fp16(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_6_cast_fp16 = add(x = reduce_mean_11_cast_fp16, y = add_6_y_0_to_fp16)[name = string("add_6_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_3_cast_fp16 = sqrt(x = add_6_cast_fp16)[name = string("sqrt_3_cast_fp16")];
tensor<fp16, [1, 32, 4, 512, 512]> real_div_3_cast_fp16 = real_div(x = sub_6_cast_fp16, y = sqrt_3_cast_fp16)[name = string("real_div_3_cast_fp16")];
tensor<int32, [4]> reshape_13_shape_0 = const()[name = string("reshape_13_shape_0"), val = tensor<int32, [4]>([1, 128, 512, 512])];
tensor<fp16, [1, 128, 512, 512]> reshape_13_cast_fp16 = reshape(shape = reshape_13_shape_0, x = real_div_3_cast_fp16)[name = string("reshape_13_cast_fp16")];
tensor<fp16, [128]> add_7_gamma_0_to_fp16 = const()[name = string("add_7_gamma_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(895808)))];
tensor<fp16, [128]> add_7_beta_0_to_fp16 = const()[name = string("add_7_beta_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(896128)))];
fp16 add_7_epsilon_0_to_fp16 = const()[name = string("add_7_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, 128, 512, 512]> add_7_cast_fp16 = batch_norm(beta = add_7_beta_0_to_fp16, epsilon = add_7_epsilon_0_to_fp16, gamma = add_7_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_13_cast_fp16)[name = string("add_7_cast_fp16")];
tensor<fp16, [1, 128, 512, 512]> input_25_cast_fp16 = silu(x = add_7_cast_fp16)[name = string("input_25_cast_fp16")];
string hidden_states_3_pad_type_0 = const()[name = string("hidden_states_3_pad_type_0"), val = string("custom")];
tensor<int32, [4]> hidden_states_3_pad_0 = const()[name = string("hidden_states_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<int32, [2]> hidden_states_3_strides_0 = const()[name = string("hidden_states_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> hidden_states_3_dilations_0 = const()[name = string("hidden_states_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 hidden_states_3_groups_0 = const()[name = string("hidden_states_3_groups_0"), val = int32(1)];
tensor<fp16, [128, 128, 3, 3]> encoder_down_blocks_0_resnets_1_conv2_weight_to_fp16 = const()[name = string("encoder_down_blocks_0_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(896448)))];
tensor<fp16, [128]> encoder_down_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = string("encoder_down_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1191424)))];
tensor<fp16, [1, 128, 512, 512]> hidden_states_3_cast_fp16 = conv(bias = encoder_down_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = hidden_states_3_dilations_0, groups = hidden_states_3_groups_0, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = hidden_states_3_strides_0, weight = encoder_down_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_25_cast_fp16)[name = string("hidden_states_3_cast_fp16")];
tensor<fp16, [1, 128, 512, 512]> var_101_cast_fp16 = add(x = var_71_cast_fp16, y = hidden_states_3_cast_fp16)[name = string("op_101_cast_fp16")];
tensor<int32, [8]> hidden_states_7_pad_0 = const()[name = string("hidden_states_7_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 1, 0, 1])];
string hidden_states_7_mode_0 = const()[name = string("hidden_states_7_mode_0"), val = string("constant")];
fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 128, 513, 513]> hidden_states_7_cast_fp16 = pad(constant_val = const_0_to_fp16, mode = hidden_states_7_mode_0, pad = hidden_states_7_pad_0, x = var_101_cast_fp16)[name = string("hidden_states_7_cast_fp16")];
string input_29_pad_type_0 = const()[name = string("input_29_pad_type_0"), val = string("valid")];
tensor<int32, [2]> input_29_strides_0 = const()[name = string("input_29_strides_0"), val = tensor<int32, [2]>([2, 2])];
tensor<int32, [4]> input_29_pad_0 = const()[name = string("input_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_29_dilations_0 = const()[name = string("input_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 input_29_groups_0 = const()[name = string("input_29_groups_0"), val = int32(1)];
tensor<fp16, [128, 128, 3, 3]> encoder_down_blocks_0_downsamplers_0_conv_weight_to_fp16 = const()[name = string("encoder_down_blocks_0_downsamplers_0_conv_weight_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1191744)))];
tensor<fp16, [128]> encoder_down_blocks_0_downsamplers_0_conv_bias_to_fp16 = const()[name = string("encoder_down_blocks_0_downsamplers_0_conv_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1486720)))];
tensor<fp16, [1, 128, 256, 256]> input_29_cast_fp16 = conv(bias = encoder_down_blocks_0_downsamplers_0_conv_bias_to_fp16, dilations = input_29_dilations_0, groups = input_29_groups_0, pad = input_29_pad_0, pad_type = input_29_pad_type_0, strides = input_29_strides_0, weight = encoder_down_blocks_0_downsamplers_0_conv_weight_to_fp16, x = hidden_states_7_cast_fp16)[name = string("input_29_cast_fp16")];
tensor<int32, [5]> reshape_16_shape_0 = const()[name = string("reshape_16_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 256, 256])];
tensor<fp16, [1, 32, 4, 256, 256]> reshape_16_cast_fp16 = reshape(shape = reshape_16_shape_0, x = input_29_cast_fp16)[name = string("reshape_16_cast_fp16")];
tensor<int32, [3]> reduce_mean_12_axes_0 = const()[name = string("reduce_mean_12_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_12_keep_dims_0 = const()[name = string("reduce_mean_12_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_12_cast_fp16 = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16_cast_fp16)[name = string("reduce_mean_12_cast_fp16")];
tensor<fp16, [1, 32, 4, 256, 256]> sub_8_cast_fp16 = sub(x = reshape_16_cast_fp16, y = reduce_mean_12_cast_fp16)[name = string("sub_8_cast_fp16")];
tensor<fp16, [1, 32, 4, 256, 256]> square_4_cast_fp16 = square(x = sub_8_cast_fp16)[name = string("square_4_cast_fp16")];
tensor<int32, [3]> reduce_mean_14_axes_0 = const()[name = string("reduce_mean_14_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_14_keep_dims_0 = const()[name = string("reduce_mean_14_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_14_cast_fp16 = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4_cast_fp16)[name = string("reduce_mean_14_cast_fp16")];
fp16 add_8_y_0_to_fp16 = const()[name = string("add_8_y_0_to_fp16"), val = fp16(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_8_cast_fp16 = add(x = reduce_mean_14_cast_fp16, y = add_8_y_0_to_fp16)[name = string("add_8_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_4_cast_fp16 = sqrt(x = add_8_cast_fp16)[name = string("sqrt_4_cast_fp16")];
tensor<fp16, [1, 32, 4, 256, 256]> real_div_4_cast_fp16 = real_div(x = sub_8_cast_fp16, y = sqrt_4_cast_fp16)[name = string("real_div_4_cast_fp16")];
tensor<int32, [4]> reshape_17_shape_0 = const()[name = string("reshape_17_shape_0"), val = tensor<int32, [4]>([1, 128, 256, 256])];
tensor<fp16, [1, 128, 256, 256]> reshape_17_cast_fp16 = reshape(shape = reshape_17_shape_0, x = real_div_4_cast_fp16)[name = string("reshape_17_cast_fp16")];
tensor<fp16, [128]> add_9_gamma_0_to_fp16 = const()[name = string("add_9_gamma_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1487040)))];
tensor<fp16, [128]> add_9_beta_0_to_fp16 = const()[name = string("add_9_beta_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1487360)))];
fp16 add_9_epsilon_0_to_fp16 = const()[name = string("add_9_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, 128, 256, 256]> add_9_cast_fp16 = batch_norm(beta = add_9_beta_0_to_fp16, epsilon = add_9_epsilon_0_to_fp16, gamma = add_9_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_17_cast_fp16)[name = string("add_9_cast_fp16")];
tensor<fp16, [1, 128, 256, 256]> input_33_cast_fp16 = silu(x = add_9_cast_fp16)[name = string("input_33_cast_fp16")];
string input_35_pad_type_0 = const()[name = string("input_35_pad_type_0"), val = string("custom")];
tensor<int32, [4]> input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<int32, [2]> input_35_strides_0 = const()[name = string("input_35_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> input_35_dilations_0 = const()[name = string("input_35_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 input_35_groups_0 = const()[name = string("input_35_groups_0"), val = int32(1)];
tensor<fp16, [256, 128, 3, 3]> encoder_down_blocks_1_resnets_0_conv1_weight_to_fp16 = const()[name = string("encoder_down_blocks_1_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [256, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1487680)))];
tensor<fp16, [256]> encoder_down_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = string("encoder_down_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2077568)))];
tensor<fp16, [1, 256, 256, 256]> input_35_cast_fp16 = conv(bias = encoder_down_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = input_35_dilations_0, groups = input_35_groups_0, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = input_35_strides_0, weight = encoder_down_blocks_1_resnets_0_conv1_weight_to_fp16, x = input_33_cast_fp16)[name = string("input_35_cast_fp16")];
tensor<int32, [5]> reshape_20_shape_0 = const()[name = string("reshape_20_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 256, 256])];
tensor<fp16, [1, 32, 8, 256, 256]> reshape_20_cast_fp16 = reshape(shape = reshape_20_shape_0, x = input_35_cast_fp16)[name = string("reshape_20_cast_fp16")];
tensor<int32, [3]> reduce_mean_15_axes_0 = const()[name = string("reduce_mean_15_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_15_keep_dims_0 = const()[name = string("reduce_mean_15_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_15_cast_fp16 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20_cast_fp16)[name = string("reduce_mean_15_cast_fp16")];
tensor<fp16, [1, 32, 8, 256, 256]> sub_10_cast_fp16 = sub(x = reshape_20_cast_fp16, y = reduce_mean_15_cast_fp16)[name = string("sub_10_cast_fp16")];
tensor<fp16, [1, 32, 8, 256, 256]> square_5_cast_fp16 = square(x = sub_10_cast_fp16)[name = string("square_5_cast_fp16")];
tensor<int32, [3]> reduce_mean_17_axes_0 = const()[name = string("reduce_mean_17_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_17_keep_dims_0 = const()[name = string("reduce_mean_17_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_17_cast_fp16 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5_cast_fp16)[name = string("reduce_mean_17_cast_fp16")];
fp16 add_10_y_0_to_fp16 = const()[name = string("add_10_y_0_to_fp16"), val = fp16(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_10_cast_fp16 = add(x = reduce_mean_17_cast_fp16, y = add_10_y_0_to_fp16)[name = string("add_10_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_5_cast_fp16 = sqrt(x = add_10_cast_fp16)[name = string("sqrt_5_cast_fp16")];
tensor<fp16, [1, 32, 8, 256, 256]> real_div_5_cast_fp16 = real_div(x = sub_10_cast_fp16, y = sqrt_5_cast_fp16)[name = string("real_div_5_cast_fp16")];
tensor<int32, [4]> reshape_21_shape_0 = const()[name = string("reshape_21_shape_0"), val = tensor<int32, [4]>([1, 256, 256, 256])];
tensor<fp16, [1, 256, 256, 256]> reshape_21_cast_fp16 = reshape(shape = reshape_21_shape_0, x = real_div_5_cast_fp16)[name = string("reshape_21_cast_fp16")];
tensor<fp16, [256]> add_11_mean_0_to_fp16 = const()[name = string("add_11_mean_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2078144)))];
tensor<fp16, [256]> add_11_variance_0_to_fp16 = const()[name = string("add_11_variance_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2078720)))];
tensor<fp16, [256]> add_11_gamma_0_to_fp16 = const()[name = string("add_11_gamma_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2079296)))];
tensor<fp16, [256]> add_11_beta_0_to_fp16 = const()[name = string("add_11_beta_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2079872)))];
fp16 add_11_epsilon_0_to_fp16 = const()[name = string("add_11_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, 256, 256, 256]> add_11_cast_fp16 = 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_fp16)[name = string("add_11_cast_fp16")];
tensor<fp16, [1, 256, 256, 256]> input_39_cast_fp16 = silu(x = add_11_cast_fp16)[name = string("input_39_cast_fp16")];
string hidden_states_9_pad_type_0 = const()[name = string("hidden_states_9_pad_type_0"), val = string("custom")];
tensor<int32, [4]> hidden_states_9_pad_0 = const()[name = string("hidden_states_9_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<int32, [2]> hidden_states_9_strides_0 = const()[name = string("hidden_states_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> hidden_states_9_dilations_0 = const()[name = string("hidden_states_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 hidden_states_9_groups_0 = const()[name = string("hidden_states_9_groups_0"), val = int32(1)];
tensor<fp16, [256, 256, 3, 3]> encoder_down_blocks_1_resnets_0_conv2_weight_to_fp16 = const()[name = string("encoder_down_blocks_1_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2080448)))];
tensor<fp16, [256]> encoder_down_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = string("encoder_down_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3260160)))];
tensor<fp16, [1, 256, 256, 256]> hidden_states_9_cast_fp16 = conv(bias = encoder_down_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = hidden_states_9_dilations_0, groups = hidden_states_9_groups_0, pad = hidden_states_9_pad_0, pad_type = hidden_states_9_pad_type_0, strides = hidden_states_9_strides_0, weight = encoder_down_blocks_1_resnets_0_conv2_weight_to_fp16, x = input_39_cast_fp16)[name = string("hidden_states_9_cast_fp16")];
string input_tensor_1_pad_type_0 = const()[name = string("input_tensor_1_pad_type_0"), val = string("valid")];
tensor<int32, [2]> input_tensor_1_strides_0 = const()[name = string("input_tensor_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> input_tensor_1_pad_0 = const()[name = string("input_tensor_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_tensor_1_dilations_0 = const()[name = string("input_tensor_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 input_tensor_1_groups_0 = const()[name = string("input_tensor_1_groups_0"), val = int32(1)];
tensor<fp16, [256, 128, 1, 1]> encoder_down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = string("encoder_down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor<fp16, [256, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3260736)))];
tensor<fp16, [256]> encoder_down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = string("encoder_down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3326336)))];
tensor<fp16, [1, 256, 256, 256]> input_tensor_1_cast_fp16 = conv(bias = encoder_down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = input_tensor_1_dilations_0, groups = input_tensor_1_groups_0, pad = input_tensor_1_pad_0, pad_type = input_tensor_1_pad_type_0, strides = input_tensor_1_strides_0, weight = encoder_down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16, x = input_29_cast_fp16)[name = string("input_tensor_1_cast_fp16")];
tensor<fp16, [1, 256, 256, 256]> var_155_cast_fp16 = add(x = input_tensor_1_cast_fp16, y = hidden_states_9_cast_fp16)[name = string("op_155_cast_fp16")];
tensor<int32, [5]> reshape_24_shape_0 = const()[name = string("reshape_24_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 256, 256])];
tensor<fp16, [1, 32, 8, 256, 256]> reshape_24_cast_fp16 = reshape(shape = reshape_24_shape_0, x = var_155_cast_fp16)[name = string("reshape_24_cast_fp16")];
tensor<int32, [3]> reduce_mean_18_axes_0 = const()[name = string("reduce_mean_18_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_18_keep_dims_0 = const()[name = string("reduce_mean_18_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_18_cast_fp16 = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24_cast_fp16)[name = string("reduce_mean_18_cast_fp16")];
tensor<fp16, [1, 32, 8, 256, 256]> sub_12_cast_fp16 = sub(x = reshape_24_cast_fp16, y = reduce_mean_18_cast_fp16)[name = string("sub_12_cast_fp16")];
tensor<fp16, [1, 32, 8, 256, 256]> square_6_cast_fp16 = square(x = sub_12_cast_fp16)[name = string("square_6_cast_fp16")];
tensor<int32, [3]> reduce_mean_20_axes_0 = const()[name = string("reduce_mean_20_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_20_keep_dims_0 = const()[name = string("reduce_mean_20_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_20_cast_fp16 = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6_cast_fp16)[name = string("reduce_mean_20_cast_fp16")];
fp16 add_12_y_0_to_fp16 = const()[name = string("add_12_y_0_to_fp16"), val = fp16(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_12_cast_fp16 = add(x = reduce_mean_20_cast_fp16, y = add_12_y_0_to_fp16)[name = string("add_12_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_6_cast_fp16 = sqrt(x = add_12_cast_fp16)[name = string("sqrt_6_cast_fp16")];
tensor<fp16, [1, 32, 8, 256, 256]> real_div_6_cast_fp16 = real_div(x = sub_12_cast_fp16, y = sqrt_6_cast_fp16)[name = string("real_div_6_cast_fp16")];
tensor<int32, [4]> reshape_25_shape_0 = const()[name = string("reshape_25_shape_0"), val = tensor<int32, [4]>([1, 256, 256, 256])];
tensor<fp16, [1, 256, 256, 256]> reshape_25_cast_fp16 = reshape(shape = reshape_25_shape_0, x = real_div_6_cast_fp16)[name = string("reshape_25_cast_fp16")];
tensor<fp16, [256]> add_13_gamma_0_to_fp16 = const()[name = string("add_13_gamma_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3326912)))];
tensor<fp16, [256]> add_13_beta_0_to_fp16 = const()[name = string("add_13_beta_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3327488)))];
fp16 add_13_epsilon_0_to_fp16 = const()[name = string("add_13_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, 256, 256, 256]> add_13_cast_fp16 = batch_norm(beta = add_13_beta_0_to_fp16, epsilon = add_13_epsilon_0_to_fp16, gamma = add_13_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_25_cast_fp16)[name = string("add_13_cast_fp16")];
tensor<fp16, [1, 256, 256, 256]> input_47_cast_fp16 = silu(x = add_13_cast_fp16)[name = string("input_47_cast_fp16")];
string input_49_pad_type_0 = const()[name = string("input_49_pad_type_0"), val = string("custom")];
tensor<int32, [4]> input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<int32, [2]> input_49_strides_0 = const()[name = string("input_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> input_49_dilations_0 = const()[name = string("input_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 input_49_groups_0 = const()[name = string("input_49_groups_0"), val = int32(1)];
tensor<fp16, [256, 256, 3, 3]> encoder_down_blocks_1_resnets_1_conv1_weight_to_fp16 = const()[name = string("encoder_down_blocks_1_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3328064)))];
tensor<fp16, [256]> encoder_down_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = string("encoder_down_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4507776)))];
tensor<fp16, [1, 256, 256, 256]> input_49_cast_fp16 = conv(bias = encoder_down_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = encoder_down_blocks_1_resnets_1_conv1_weight_to_fp16, x = input_47_cast_fp16)[name = string("input_49_cast_fp16")];
tensor<int32, [5]> reshape_28_shape_0 = const()[name = string("reshape_28_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 256, 256])];
tensor<fp16, [1, 32, 8, 256, 256]> reshape_28_cast_fp16 = reshape(shape = reshape_28_shape_0, x = input_49_cast_fp16)[name = string("reshape_28_cast_fp16")];
tensor<int32, [3]> reduce_mean_21_axes_0 = const()[name = string("reduce_mean_21_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_21_keep_dims_0 = const()[name = string("reduce_mean_21_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_21_cast_fp16 = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28_cast_fp16)[name = string("reduce_mean_21_cast_fp16")];
tensor<fp16, [1, 32, 8, 256, 256]> sub_14_cast_fp16 = sub(x = reshape_28_cast_fp16, y = reduce_mean_21_cast_fp16)[name = string("sub_14_cast_fp16")];
tensor<fp16, [1, 32, 8, 256, 256]> square_7_cast_fp16 = square(x = sub_14_cast_fp16)[name = string("square_7_cast_fp16")];
tensor<int32, [3]> reduce_mean_23_axes_0 = const()[name = string("reduce_mean_23_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_23_keep_dims_0 = const()[name = string("reduce_mean_23_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_23_cast_fp16 = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7_cast_fp16)[name = string("reduce_mean_23_cast_fp16")];
fp16 add_14_y_0_to_fp16 = const()[name = string("add_14_y_0_to_fp16"), val = fp16(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_14_cast_fp16 = add(x = reduce_mean_23_cast_fp16, y = add_14_y_0_to_fp16)[name = string("add_14_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_7_cast_fp16 = sqrt(x = add_14_cast_fp16)[name = string("sqrt_7_cast_fp16")];
tensor<fp16, [1, 32, 8, 256, 256]> real_div_7_cast_fp16 = real_div(x = sub_14_cast_fp16, y = sqrt_7_cast_fp16)[name = string("real_div_7_cast_fp16")];
tensor<int32, [4]> reshape_29_shape_0 = const()[name = string("reshape_29_shape_0"), val = tensor<int32, [4]>([1, 256, 256, 256])];
tensor<fp16, [1, 256, 256, 256]> reshape_29_cast_fp16 = reshape(shape = reshape_29_shape_0, x = real_div_7_cast_fp16)[name = string("reshape_29_cast_fp16")];
tensor<fp16, [256]> add_15_gamma_0_to_fp16 = const()[name = string("add_15_gamma_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4508352)))];
tensor<fp16, [256]> add_15_beta_0_to_fp16 = const()[name = string("add_15_beta_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4508928)))];
fp16 add_15_epsilon_0_to_fp16 = const()[name = string("add_15_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, 256, 256, 256]> add_15_cast_fp16 = batch_norm(beta = add_15_beta_0_to_fp16, epsilon = add_15_epsilon_0_to_fp16, gamma = add_15_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_29_cast_fp16)[name = string("add_15_cast_fp16")];
tensor<fp16, [1, 256, 256, 256]> input_53_cast_fp16 = silu(x = add_15_cast_fp16)[name = string("input_53_cast_fp16")];
string hidden_states_11_pad_type_0 = const()[name = string("hidden_states_11_pad_type_0"), val = string("custom")];
tensor<int32, [4]> hidden_states_11_pad_0 = const()[name = string("hidden_states_11_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<int32, [2]> hidden_states_11_strides_0 = const()[name = string("hidden_states_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> hidden_states_11_dilations_0 = const()[name = string("hidden_states_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 hidden_states_11_groups_0 = const()[name = string("hidden_states_11_groups_0"), val = int32(1)];
tensor<fp16, [256, 256, 3, 3]> encoder_down_blocks_1_resnets_1_conv2_weight_to_fp16 = const()[name = string("encoder_down_blocks_1_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4509504)))];
tensor<fp16, [256]> encoder_down_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = string("encoder_down_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689216)))];
tensor<fp16, [1, 256, 256, 256]> hidden_states_11_cast_fp16 = conv(bias = encoder_down_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = hidden_states_11_dilations_0, groups = hidden_states_11_groups_0, pad = hidden_states_11_pad_0, pad_type = hidden_states_11_pad_type_0, strides = hidden_states_11_strides_0, weight = encoder_down_blocks_1_resnets_1_conv2_weight_to_fp16, x = input_53_cast_fp16)[name = string("hidden_states_11_cast_fp16")];
tensor<fp16, [1, 256, 256, 256]> var_185_cast_fp16 = add(x = var_155_cast_fp16, y = hidden_states_11_cast_fp16)[name = string("op_185_cast_fp16")];
tensor<int32, [8]> hidden_states_15_pad_0 = const()[name = string("hidden_states_15_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 1, 0, 1])];
string hidden_states_15_mode_0 = const()[name = string("hidden_states_15_mode_0"), val = string("constant")];
fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 256, 257, 257]> hidden_states_15_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = hidden_states_15_mode_0, pad = hidden_states_15_pad_0, x = var_185_cast_fp16)[name = string("hidden_states_15_cast_fp16")];
string input_57_pad_type_0 = const()[name = string("input_57_pad_type_0"), val = string("valid")];
tensor<int32, [2]> input_57_strides_0 = const()[name = string("input_57_strides_0"), val = tensor<int32, [2]>([2, 2])];
tensor<int32, [4]> input_57_pad_0 = const()[name = string("input_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_57_dilations_0 = const()[name = string("input_57_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 input_57_groups_0 = const()[name = string("input_57_groups_0"), val = int32(1)];
tensor<fp16, [256, 256, 3, 3]> encoder_down_blocks_1_downsamplers_0_conv_weight_to_fp16 = const()[name = string("encoder_down_blocks_1_downsamplers_0_conv_weight_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689792)))];
tensor<fp16, [256]> encoder_down_blocks_1_downsamplers_0_conv_bias_to_fp16 = const()[name = string("encoder_down_blocks_1_downsamplers_0_conv_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6869504)))];
tensor<fp16, [1, 256, 128, 128]> input_57_cast_fp16 = conv(bias = encoder_down_blocks_1_downsamplers_0_conv_bias_to_fp16, dilations = input_57_dilations_0, groups = input_57_groups_0, pad = input_57_pad_0, pad_type = input_57_pad_type_0, strides = input_57_strides_0, weight = encoder_down_blocks_1_downsamplers_0_conv_weight_to_fp16, x = hidden_states_15_cast_fp16)[name = string("input_57_cast_fp16")];
tensor<int32, [5]> reshape_32_shape_0 = const()[name = string("reshape_32_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 128, 128])];
tensor<fp16, [1, 32, 8, 128, 128]> reshape_32_cast_fp16 = reshape(shape = reshape_32_shape_0, x = input_57_cast_fp16)[name = string("reshape_32_cast_fp16")];
tensor<int32, [3]> reduce_mean_24_axes_0 = const()[name = string("reduce_mean_24_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_24_keep_dims_0 = const()[name = string("reduce_mean_24_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_24_cast_fp16 = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32_cast_fp16)[name = string("reduce_mean_24_cast_fp16")];
tensor<fp16, [1, 32, 8, 128, 128]> sub_16_cast_fp16 = sub(x = reshape_32_cast_fp16, y = reduce_mean_24_cast_fp16)[name = string("sub_16_cast_fp16")];
tensor<fp16, [1, 32, 8, 128, 128]> square_8_cast_fp16 = square(x = sub_16_cast_fp16)[name = string("square_8_cast_fp16")];
tensor<int32, [3]> reduce_mean_26_axes_0 = const()[name = string("reduce_mean_26_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_26_keep_dims_0 = const()[name = string("reduce_mean_26_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_26_cast_fp16 = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8_cast_fp16)[name = string("reduce_mean_26_cast_fp16")];
fp16 add_16_y_0_to_fp16 = const()[name = string("add_16_y_0_to_fp16"), val = fp16(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_16_cast_fp16 = add(x = reduce_mean_26_cast_fp16, y = add_16_y_0_to_fp16)[name = string("add_16_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_8_cast_fp16 = sqrt(x = add_16_cast_fp16)[name = string("sqrt_8_cast_fp16")];
tensor<fp16, [1, 32, 8, 128, 128]> real_div_8_cast_fp16 = real_div(x = sub_16_cast_fp16, y = sqrt_8_cast_fp16)[name = string("real_div_8_cast_fp16")];
tensor<int32, [4]> reshape_33_shape_0 = const()[name = string("reshape_33_shape_0"), val = tensor<int32, [4]>([1, 256, 128, 128])];
tensor<fp16, [1, 256, 128, 128]> reshape_33_cast_fp16 = reshape(shape = reshape_33_shape_0, x = real_div_8_cast_fp16)[name = string("reshape_33_cast_fp16")];
tensor<fp16, [256]> add_17_gamma_0_to_fp16 = const()[name = string("add_17_gamma_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6870080)))];
tensor<fp16, [256]> add_17_beta_0_to_fp16 = const()[name = string("add_17_beta_0_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6870656)))];
fp16 add_17_epsilon_0_to_fp16 = const()[name = string("add_17_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, 256, 128, 128]> add_17_cast_fp16 = batch_norm(beta = add_17_beta_0_to_fp16, epsilon = add_17_epsilon_0_to_fp16, gamma = add_17_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_33_cast_fp16)[name = string("add_17_cast_fp16")];
tensor<fp16, [1, 256, 128, 128]> input_61_cast_fp16 = silu(x = add_17_cast_fp16)[name = string("input_61_cast_fp16")];
string input_63_pad_type_0 = const()[name = string("input_63_pad_type_0"), val = string("custom")];
tensor<int32, [4]> input_63_pad_0 = const()[name = string("input_63_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<int32, [2]> input_63_strides_0 = const()[name = string("input_63_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> input_63_dilations_0 = const()[name = string("input_63_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 input_63_groups_0 = const()[name = string("input_63_groups_0"), val = int32(1)];
tensor<fp16, [512, 256, 3, 3]> encoder_down_blocks_2_resnets_0_conv1_weight_to_fp16 = const()[name = string("encoder_down_blocks_2_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [512, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6871232)))];
tensor<fp16, [512]> encoder_down_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = string("encoder_down_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9230592)))];
tensor<fp16, [1, 512, 128, 128]> input_63_cast_fp16 = conv(bias = encoder_down_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = input_63_dilations_0, groups = input_63_groups_0, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = input_63_strides_0, weight = encoder_down_blocks_2_resnets_0_conv1_weight_to_fp16, x = input_61_cast_fp16)[name = string("input_63_cast_fp16")];
tensor<int32, [5]> reshape_36_shape_0 = const()[name = string("reshape_36_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])];
tensor<fp16, [1, 32, 16, 128, 128]> reshape_36_cast_fp16 = reshape(shape = reshape_36_shape_0, x = input_63_cast_fp16)[name = string("reshape_36_cast_fp16")];
tensor<int32, [3]> reduce_mean_27_axes_0 = const()[name = string("reduce_mean_27_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_27_keep_dims_0 = const()[name = string("reduce_mean_27_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_27_cast_fp16 = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36_cast_fp16)[name = string("reduce_mean_27_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 128]> sub_18_cast_fp16 = sub(x = reshape_36_cast_fp16, y = reduce_mean_27_cast_fp16)[name = string("sub_18_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 128]> square_9_cast_fp16 = square(x = sub_18_cast_fp16)[name = string("square_9_cast_fp16")];
tensor<int32, [3]> reduce_mean_29_axes_0 = const()[name = string("reduce_mean_29_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_29_keep_dims_0 = const()[name = string("reduce_mean_29_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_29_cast_fp16 = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9_cast_fp16)[name = string("reduce_mean_29_cast_fp16")];
fp16 add_18_y_0_to_fp16 = const()[name = string("add_18_y_0_to_fp16"), val = fp16(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_18_cast_fp16 = add(x = reduce_mean_29_cast_fp16, y = add_18_y_0_to_fp16)[name = string("add_18_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_9_cast_fp16 = sqrt(x = add_18_cast_fp16)[name = string("sqrt_9_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 128]> real_div_9_cast_fp16 = real_div(x = sub_18_cast_fp16, y = sqrt_9_cast_fp16)[name = string("real_div_9_cast_fp16")];
tensor<int32, [4]> reshape_37_shape_0 = const()[name = string("reshape_37_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])];
tensor<fp16, [1, 512, 128, 128]> reshape_37_cast_fp16 = reshape(shape = reshape_37_shape_0, x = real_div_9_cast_fp16)[name = string("reshape_37_cast_fp16")];
tensor<fp16, [512]> add_19_mean_0_to_fp16 = const()[name = string("add_19_mean_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9231680)))];
tensor<fp16, [512]> add_19_variance_0_to_fp16 = const()[name = string("add_19_variance_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9232768)))];
tensor<fp16, [512]> add_19_gamma_0_to_fp16 = const()[name = string("add_19_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9233856)))];
tensor<fp16, [512]> add_19_beta_0_to_fp16 = const()[name = string("add_19_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9234944)))];
fp16 add_19_epsilon_0_to_fp16 = const()[name = string("add_19_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, 512, 128, 128]> add_19_cast_fp16 = 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_fp16)[name = string("add_19_cast_fp16")];
tensor<fp16, [1, 512, 128, 128]> input_67_cast_fp16 = silu(x = add_19_cast_fp16)[name = string("input_67_cast_fp16")];
string hidden_states_17_pad_type_0 = const()[name = string("hidden_states_17_pad_type_0"), val = string("custom")];
tensor<int32, [4]> hidden_states_17_pad_0 = const()[name = string("hidden_states_17_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<int32, [2]> hidden_states_17_strides_0 = const()[name = string("hidden_states_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> hidden_states_17_dilations_0 = const()[name = string("hidden_states_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 hidden_states_17_groups_0 = const()[name = string("hidden_states_17_groups_0"), val = int32(1)];
tensor<fp16, [512, 512, 3, 3]> encoder_down_blocks_2_resnets_0_conv2_weight_to_fp16 = const()[name = string("encoder_down_blocks_2_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9236032)))];
tensor<fp16, [512]> encoder_down_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = string("encoder_down_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13954688)))];
tensor<fp16, [1, 512, 128, 128]> hidden_states_17_cast_fp16 = conv(bias = encoder_down_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = hidden_states_17_dilations_0, groups = hidden_states_17_groups_0, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = hidden_states_17_strides_0, weight = encoder_down_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_67_cast_fp16)[name = string("hidden_states_17_cast_fp16")];
string input_tensor_pad_type_0 = const()[name = string("input_tensor_pad_type_0"), val = string("valid")];
tensor<int32, [2]> input_tensor_strides_0 = const()[name = string("input_tensor_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> input_tensor_pad_0 = const()[name = string("input_tensor_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_tensor_dilations_0 = const()[name = string("input_tensor_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 input_tensor_groups_0 = const()[name = string("input_tensor_groups_0"), val = int32(1)];
tensor<fp16, [512, 256, 1, 1]> encoder_down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = string("encoder_down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor<fp16, [512, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13955776)))];
tensor<fp16, [512]> encoder_down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = string("encoder_down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14217984)))];
tensor<fp16, [1, 512, 128, 128]> input_tensor_cast_fp16 = conv(bias = encoder_down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = input_tensor_dilations_0, groups = input_tensor_groups_0, pad = input_tensor_pad_0, pad_type = input_tensor_pad_type_0, strides = input_tensor_strides_0, weight = encoder_down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16, x = input_57_cast_fp16)[name = string("input_tensor_cast_fp16")];
tensor<fp16, [1, 512, 128, 128]> var_239_cast_fp16 = add(x = input_tensor_cast_fp16, y = hidden_states_17_cast_fp16)[name = string("op_239_cast_fp16")];
tensor<int32, [5]> reshape_40_shape_0 = const()[name = string("reshape_40_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])];
tensor<fp16, [1, 32, 16, 128, 128]> reshape_40_cast_fp16 = reshape(shape = reshape_40_shape_0, x = var_239_cast_fp16)[name = string("reshape_40_cast_fp16")];
tensor<int32, [3]> reduce_mean_30_axes_0 = const()[name = string("reduce_mean_30_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_30_keep_dims_0 = const()[name = string("reduce_mean_30_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_30_cast_fp16 = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40_cast_fp16)[name = string("reduce_mean_30_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 128]> sub_20_cast_fp16 = sub(x = reshape_40_cast_fp16, y = reduce_mean_30_cast_fp16)[name = string("sub_20_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 128]> square_10_cast_fp16 = square(x = sub_20_cast_fp16)[name = string("square_10_cast_fp16")];
tensor<int32, [3]> reduce_mean_32_axes_0 = const()[name = string("reduce_mean_32_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_32_keep_dims_0 = const()[name = string("reduce_mean_32_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_32_cast_fp16 = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10_cast_fp16)[name = string("reduce_mean_32_cast_fp16")];
fp16 add_20_y_0_to_fp16 = const()[name = string("add_20_y_0_to_fp16"), val = fp16(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_20_cast_fp16 = add(x = reduce_mean_32_cast_fp16, y = add_20_y_0_to_fp16)[name = string("add_20_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_10_cast_fp16 = sqrt(x = add_20_cast_fp16)[name = string("sqrt_10_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 128]> real_div_10_cast_fp16 = real_div(x = sub_20_cast_fp16, y = sqrt_10_cast_fp16)[name = string("real_div_10_cast_fp16")];
tensor<int32, [4]> reshape_41_shape_0 = const()[name = string("reshape_41_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])];
tensor<fp16, [1, 512, 128, 128]> reshape_41_cast_fp16 = reshape(shape = reshape_41_shape_0, x = real_div_10_cast_fp16)[name = string("reshape_41_cast_fp16")];
tensor<fp16, [512]> add_21_gamma_0_to_fp16 = const()[name = string("add_21_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14219072)))];
tensor<fp16, [512]> add_21_beta_0_to_fp16 = const()[name = string("add_21_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14220160)))];
fp16 add_21_epsilon_0_to_fp16 = const()[name = string("add_21_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, 512, 128, 128]> add_21_cast_fp16 = batch_norm(beta = add_21_beta_0_to_fp16, epsilon = add_21_epsilon_0_to_fp16, gamma = add_21_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_41_cast_fp16)[name = string("add_21_cast_fp16")];
tensor<fp16, [1, 512, 128, 128]> input_75_cast_fp16 = silu(x = add_21_cast_fp16)[name = string("input_75_cast_fp16")];
string input_77_pad_type_0 = const()[name = string("input_77_pad_type_0"), val = string("custom")];
tensor<int32, [4]> input_77_pad_0 = const()[name = string("input_77_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<int32, [2]> input_77_strides_0 = const()[name = string("input_77_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> input_77_dilations_0 = const()[name = string("input_77_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 input_77_groups_0 = const()[name = string("input_77_groups_0"), val = int32(1)];
tensor<fp16, [512, 512, 3, 3]> encoder_down_blocks_2_resnets_1_conv1_weight_to_fp16 = const()[name = string("encoder_down_blocks_2_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14221248)))];
tensor<fp16, [512]> encoder_down_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = string("encoder_down_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18939904)))];
tensor<fp16, [1, 512, 128, 128]> input_77_cast_fp16 = conv(bias = encoder_down_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = input_77_dilations_0, groups = input_77_groups_0, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = input_77_strides_0, weight = encoder_down_blocks_2_resnets_1_conv1_weight_to_fp16, x = input_75_cast_fp16)[name = string("input_77_cast_fp16")];
tensor<int32, [5]> reshape_44_shape_0 = const()[name = string("reshape_44_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])];
tensor<fp16, [1, 32, 16, 128, 128]> reshape_44_cast_fp16 = reshape(shape = reshape_44_shape_0, x = input_77_cast_fp16)[name = string("reshape_44_cast_fp16")];
tensor<int32, [3]> reduce_mean_33_axes_0 = const()[name = string("reduce_mean_33_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_33_keep_dims_0 = const()[name = string("reduce_mean_33_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_33_cast_fp16 = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44_cast_fp16)[name = string("reduce_mean_33_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 128]> sub_22_cast_fp16 = sub(x = reshape_44_cast_fp16, y = reduce_mean_33_cast_fp16)[name = string("sub_22_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 128]> square_11_cast_fp16 = square(x = sub_22_cast_fp16)[name = string("square_11_cast_fp16")];
tensor<int32, [3]> reduce_mean_35_axes_0 = const()[name = string("reduce_mean_35_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_35_keep_dims_0 = const()[name = string("reduce_mean_35_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_35_cast_fp16 = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11_cast_fp16)[name = string("reduce_mean_35_cast_fp16")];
fp16 add_22_y_0_to_fp16 = const()[name = string("add_22_y_0_to_fp16"), val = fp16(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_22_cast_fp16 = add(x = reduce_mean_35_cast_fp16, y = add_22_y_0_to_fp16)[name = string("add_22_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_11_cast_fp16 = sqrt(x = add_22_cast_fp16)[name = string("sqrt_11_cast_fp16")];
tensor<fp16, [1, 32, 16, 128, 128]> real_div_11_cast_fp16 = real_div(x = sub_22_cast_fp16, y = sqrt_11_cast_fp16)[name = string("real_div_11_cast_fp16")];
tensor<int32, [4]> reshape_45_shape_0 = const()[name = string("reshape_45_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])];
tensor<fp16, [1, 512, 128, 128]> reshape_45_cast_fp16 = reshape(shape = reshape_45_shape_0, x = real_div_11_cast_fp16)[name = string("reshape_45_cast_fp16")];
tensor<fp16, [512]> add_23_gamma_0_to_fp16 = const()[name = string("add_23_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18940992)))];
tensor<fp16, [512]> add_23_beta_0_to_fp16 = const()[name = string("add_23_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18942080)))];
fp16 add_23_epsilon_0_to_fp16 = const()[name = string("add_23_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, 512, 128, 128]> add_23_cast_fp16 = batch_norm(beta = add_23_beta_0_to_fp16, epsilon = add_23_epsilon_0_to_fp16, gamma = add_23_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_45_cast_fp16)[name = string("add_23_cast_fp16")];
tensor<fp16, [1, 512, 128, 128]> input_81_cast_fp16 = silu(x = add_23_cast_fp16)[name = string("input_81_cast_fp16")];
string hidden_states_19_pad_type_0 = const()[name = string("hidden_states_19_pad_type_0"), val = string("custom")];
tensor<int32, [4]> hidden_states_19_pad_0 = const()[name = string("hidden_states_19_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<int32, [2]> hidden_states_19_strides_0 = const()[name = string("hidden_states_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> hidden_states_19_dilations_0 = const()[name = string("hidden_states_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 hidden_states_19_groups_0 = const()[name = string("hidden_states_19_groups_0"), val = int32(1)];
tensor<fp16, [512, 512, 3, 3]> encoder_down_blocks_2_resnets_1_conv2_weight_to_fp16 = const()[name = string("encoder_down_blocks_2_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18943168)))];
tensor<fp16, [512]> encoder_down_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = string("encoder_down_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23661824)))];
tensor<fp16, [1, 512, 128, 128]> hidden_states_19_cast_fp16 = conv(bias = encoder_down_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = hidden_states_19_dilations_0, groups = hidden_states_19_groups_0, pad = hidden_states_19_pad_0, pad_type = hidden_states_19_pad_type_0, strides = hidden_states_19_strides_0, weight = encoder_down_blocks_2_resnets_1_conv2_weight_to_fp16, x = input_81_cast_fp16)[name = string("hidden_states_19_cast_fp16")];
tensor<fp16, [1, 512, 128, 128]> var_269_cast_fp16 = add(x = var_239_cast_fp16, y = hidden_states_19_cast_fp16)[name = string("op_269_cast_fp16")];
tensor<int32, [8]> hidden_states_23_pad_0 = const()[name = string("hidden_states_23_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 1, 0, 1])];
string hidden_states_23_mode_0 = const()[name = string("hidden_states_23_mode_0"), val = string("constant")];
fp16 const_2_to_fp16 = const()[name = string("const_2_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 512, 129, 129]> hidden_states_23_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = hidden_states_23_mode_0, pad = hidden_states_23_pad_0, x = var_269_cast_fp16)[name = string("hidden_states_23_cast_fp16")];
string input_85_pad_type_0 = const()[name = string("input_85_pad_type_0"), val = string("valid")];
tensor<int32, [2]> input_85_strides_0 = const()[name = string("input_85_strides_0"), val = tensor<int32, [2]>([2, 2])];
tensor<int32, [4]> input_85_pad_0 = const()[name = string("input_85_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> input_85_dilations_0 = const()[name = string("input_85_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 input_85_groups_0 = const()[name = string("input_85_groups_0"), val = int32(1)];
tensor<fp16, [512, 512, 3, 3]> encoder_down_blocks_2_downsamplers_0_conv_weight_to_fp16 = const()[name = string("encoder_down_blocks_2_downsamplers_0_conv_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23662912)))];
tensor<fp16, [512]> encoder_down_blocks_2_downsamplers_0_conv_bias_to_fp16 = const()[name = string("encoder_down_blocks_2_downsamplers_0_conv_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28381568)))];
tensor<fp16, [1, 512, 64, 64]> input_85_cast_fp16 = conv(bias = encoder_down_blocks_2_downsamplers_0_conv_bias_to_fp16, dilations = input_85_dilations_0, groups = input_85_groups_0, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = input_85_strides_0, weight = encoder_down_blocks_2_downsamplers_0_conv_weight_to_fp16, x = hidden_states_23_cast_fp16)[name = string("input_85_cast_fp16")];
tensor<int32, [5]> reshape_48_shape_0 = const()[name = string("reshape_48_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])];
tensor<fp16, [1, 32, 16, 64, 64]> reshape_48_cast_fp16 = reshape(shape = reshape_48_shape_0, x = input_85_cast_fp16)[name = string("reshape_48_cast_fp16")];
tensor<int32, [3]> reduce_mean_36_axes_0 = const()[name = string("reduce_mean_36_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_36_keep_dims_0 = const()[name = string("reduce_mean_36_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_36_cast_fp16 = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48_cast_fp16)[name = string("reduce_mean_36_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> sub_24_cast_fp16 = sub(x = reshape_48_cast_fp16, y = reduce_mean_36_cast_fp16)[name = string("sub_24_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> square_12_cast_fp16 = square(x = sub_24_cast_fp16)[name = string("square_12_cast_fp16")];
tensor<int32, [3]> reduce_mean_38_axes_0 = const()[name = string("reduce_mean_38_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_38_keep_dims_0 = const()[name = string("reduce_mean_38_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_38_cast_fp16 = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12_cast_fp16)[name = string("reduce_mean_38_cast_fp16")];
fp16 add_24_y_0_to_fp16 = const()[name = string("add_24_y_0_to_fp16"), val = fp16(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_24_cast_fp16 = add(x = reduce_mean_38_cast_fp16, y = add_24_y_0_to_fp16)[name = string("add_24_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_12_cast_fp16 = sqrt(x = add_24_cast_fp16)[name = string("sqrt_12_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> real_div_12_cast_fp16 = real_div(x = sub_24_cast_fp16, y = sqrt_12_cast_fp16)[name = string("real_div_12_cast_fp16")];
tensor<int32, [4]> reshape_49_shape_0 = const()[name = string("reshape_49_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])];
tensor<fp16, [1, 512, 64, 64]> reshape_49_cast_fp16 = reshape(shape = reshape_49_shape_0, x = real_div_12_cast_fp16)[name = string("reshape_49_cast_fp16")];
tensor<fp16, [512]> add_25_gamma_0_to_fp16 = const()[name = string("add_25_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28382656)))];
tensor<fp16, [512]> add_25_beta_0_to_fp16 = const()[name = string("add_25_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28383744)))];
fp16 add_25_epsilon_0_to_fp16 = const()[name = string("add_25_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, 512, 64, 64]> add_25_cast_fp16 = batch_norm(beta = add_25_beta_0_to_fp16, epsilon = add_25_epsilon_0_to_fp16, gamma = add_25_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_49_cast_fp16)[name = string("add_25_cast_fp16")];
tensor<fp16, [1, 512, 64, 64]> input_89_cast_fp16 = silu(x = add_25_cast_fp16)[name = string("input_89_cast_fp16")];
string input_91_pad_type_0 = const()[name = string("input_91_pad_type_0"), val = string("custom")];
tensor<int32, [4]> input_91_pad_0 = const()[name = string("input_91_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<int32, [2]> input_91_strides_0 = const()[name = string("input_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> input_91_dilations_0 = const()[name = string("input_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 input_91_groups_0 = const()[name = string("input_91_groups_0"), val = int32(1)];
tensor<fp16, [512, 512, 3, 3]> encoder_down_blocks_3_resnets_0_conv1_weight_to_fp16 = const()[name = string("encoder_down_blocks_3_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(28384832)))];
tensor<fp16, [512]> encoder_down_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = string("encoder_down_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33103488)))];
tensor<fp16, [1, 512, 64, 64]> input_91_cast_fp16 = conv(bias = encoder_down_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = input_91_dilations_0, groups = input_91_groups_0, pad = input_91_pad_0, pad_type = input_91_pad_type_0, strides = input_91_strides_0, weight = encoder_down_blocks_3_resnets_0_conv1_weight_to_fp16, x = input_89_cast_fp16)[name = string("input_91_cast_fp16")];
tensor<int32, [5]> reshape_52_shape_0 = const()[name = string("reshape_52_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])];
tensor<fp16, [1, 32, 16, 64, 64]> reshape_52_cast_fp16 = reshape(shape = reshape_52_shape_0, x = input_91_cast_fp16)[name = string("reshape_52_cast_fp16")];
tensor<int32, [3]> reduce_mean_39_axes_0 = const()[name = string("reduce_mean_39_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_39_keep_dims_0 = const()[name = string("reduce_mean_39_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_39_cast_fp16 = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52_cast_fp16)[name = string("reduce_mean_39_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> sub_26_cast_fp16 = sub(x = reshape_52_cast_fp16, y = reduce_mean_39_cast_fp16)[name = string("sub_26_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> square_13_cast_fp16 = square(x = sub_26_cast_fp16)[name = string("square_13_cast_fp16")];
tensor<int32, [3]> reduce_mean_41_axes_0 = const()[name = string("reduce_mean_41_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_41_keep_dims_0 = const()[name = string("reduce_mean_41_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_41_cast_fp16 = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13_cast_fp16)[name = string("reduce_mean_41_cast_fp16")];
fp16 add_26_y_0_to_fp16 = const()[name = string("add_26_y_0_to_fp16"), val = fp16(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_26_cast_fp16 = add(x = reduce_mean_41_cast_fp16, y = add_26_y_0_to_fp16)[name = string("add_26_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_13_cast_fp16 = sqrt(x = add_26_cast_fp16)[name = string("sqrt_13_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> real_div_13_cast_fp16 = real_div(x = sub_26_cast_fp16, y = sqrt_13_cast_fp16)[name = string("real_div_13_cast_fp16")];
tensor<int32, [4]> reshape_53_shape_0 = const()[name = string("reshape_53_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])];
tensor<fp16, [1, 512, 64, 64]> reshape_53_cast_fp16 = reshape(shape = reshape_53_shape_0, x = real_div_13_cast_fp16)[name = string("reshape_53_cast_fp16")];
tensor<fp16, [512]> add_27_gamma_0_to_fp16 = const()[name = string("add_27_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33104576)))];
tensor<fp16, [512]> add_27_beta_0_to_fp16 = const()[name = string("add_27_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33105664)))];
fp16 add_27_epsilon_0_to_fp16 = const()[name = string("add_27_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, 512, 64, 64]> add_27_cast_fp16 = batch_norm(beta = add_27_beta_0_to_fp16, epsilon = add_27_epsilon_0_to_fp16, gamma = add_27_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_53_cast_fp16)[name = string("add_27_cast_fp16")];
tensor<fp16, [1, 512, 64, 64]> input_95_cast_fp16 = silu(x = add_27_cast_fp16)[name = string("input_95_cast_fp16")];
string hidden_states_25_pad_type_0 = const()[name = string("hidden_states_25_pad_type_0"), val = string("custom")];
tensor<int32, [4]> hidden_states_25_pad_0 = const()[name = string("hidden_states_25_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<int32, [2]> hidden_states_25_strides_0 = const()[name = string("hidden_states_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> hidden_states_25_dilations_0 = const()[name = string("hidden_states_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 hidden_states_25_groups_0 = const()[name = string("hidden_states_25_groups_0"), val = int32(1)];
tensor<fp16, [512, 512, 3, 3]> encoder_down_blocks_3_resnets_0_conv2_weight_to_fp16 = const()[name = string("encoder_down_blocks_3_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33106752)))];
tensor<fp16, [512]> encoder_down_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = string("encoder_down_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37825408)))];
tensor<fp16, [1, 512, 64, 64]> hidden_states_25_cast_fp16 = conv(bias = encoder_down_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = hidden_states_25_dilations_0, groups = hidden_states_25_groups_0, pad = hidden_states_25_pad_0, pad_type = hidden_states_25_pad_type_0, strides = hidden_states_25_strides_0, weight = encoder_down_blocks_3_resnets_0_conv2_weight_to_fp16, x = input_95_cast_fp16)[name = string("hidden_states_25_cast_fp16")];
tensor<fp16, [1, 512, 64, 64]> var_313_cast_fp16 = add(x = input_85_cast_fp16, y = hidden_states_25_cast_fp16)[name = string("op_313_cast_fp16")];
tensor<int32, [5]> reshape_56_shape_0 = const()[name = string("reshape_56_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])];
tensor<fp16, [1, 32, 16, 64, 64]> reshape_56_cast_fp16 = reshape(shape = reshape_56_shape_0, x = var_313_cast_fp16)[name = string("reshape_56_cast_fp16")];
tensor<int32, [3]> reduce_mean_42_axes_0 = const()[name = string("reduce_mean_42_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_42_keep_dims_0 = const()[name = string("reduce_mean_42_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_42_cast_fp16 = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56_cast_fp16)[name = string("reduce_mean_42_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> sub_28_cast_fp16 = sub(x = reshape_56_cast_fp16, y = reduce_mean_42_cast_fp16)[name = string("sub_28_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> square_14_cast_fp16 = square(x = sub_28_cast_fp16)[name = string("square_14_cast_fp16")];
tensor<int32, [3]> reduce_mean_44_axes_0 = const()[name = string("reduce_mean_44_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_44_keep_dims_0 = const()[name = string("reduce_mean_44_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_44_cast_fp16 = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14_cast_fp16)[name = string("reduce_mean_44_cast_fp16")];
fp16 add_28_y_0_to_fp16 = const()[name = string("add_28_y_0_to_fp16"), val = fp16(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_28_cast_fp16 = add(x = reduce_mean_44_cast_fp16, y = add_28_y_0_to_fp16)[name = string("add_28_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_14_cast_fp16 = sqrt(x = add_28_cast_fp16)[name = string("sqrt_14_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> real_div_14_cast_fp16 = real_div(x = sub_28_cast_fp16, y = sqrt_14_cast_fp16)[name = string("real_div_14_cast_fp16")];
tensor<int32, [4]> reshape_57_shape_0 = const()[name = string("reshape_57_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])];
tensor<fp16, [1, 512, 64, 64]> reshape_57_cast_fp16 = reshape(shape = reshape_57_shape_0, x = real_div_14_cast_fp16)[name = string("reshape_57_cast_fp16")];
tensor<fp16, [512]> add_29_gamma_0_to_fp16 = const()[name = string("add_29_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37826496)))];
tensor<fp16, [512]> add_29_beta_0_to_fp16 = const()[name = string("add_29_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37827584)))];
fp16 add_29_epsilon_0_to_fp16 = const()[name = string("add_29_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, 512, 64, 64]> add_29_cast_fp16 = batch_norm(beta = add_29_beta_0_to_fp16, epsilon = add_29_epsilon_0_to_fp16, gamma = add_29_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_57_cast_fp16)[name = string("add_29_cast_fp16")];
tensor<fp16, [1, 512, 64, 64]> input_103_cast_fp16 = silu(x = add_29_cast_fp16)[name = string("input_103_cast_fp16")];
string input_105_pad_type_0 = const()[name = string("input_105_pad_type_0"), val = string("custom")];
tensor<int32, [4]> input_105_pad_0 = const()[name = string("input_105_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<int32, [2]> input_105_strides_0 = const()[name = string("input_105_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> input_105_dilations_0 = const()[name = string("input_105_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 input_105_groups_0 = const()[name = string("input_105_groups_0"), val = int32(1)];
tensor<fp16, [512, 512, 3, 3]> encoder_down_blocks_3_resnets_1_conv1_weight_to_fp16 = const()[name = string("encoder_down_blocks_3_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37828672)))];
tensor<fp16, [512]> encoder_down_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = string("encoder_down_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42547328)))];
tensor<fp16, [1, 512, 64, 64]> input_105_cast_fp16 = conv(bias = encoder_down_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = input_105_dilations_0, groups = input_105_groups_0, pad = input_105_pad_0, pad_type = input_105_pad_type_0, strides = input_105_strides_0, weight = encoder_down_blocks_3_resnets_1_conv1_weight_to_fp16, x = input_103_cast_fp16)[name = string("input_105_cast_fp16")];
tensor<int32, [5]> reshape_60_shape_0 = const()[name = string("reshape_60_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])];
tensor<fp16, [1, 32, 16, 64, 64]> reshape_60_cast_fp16 = reshape(shape = reshape_60_shape_0, x = input_105_cast_fp16)[name = string("reshape_60_cast_fp16")];
tensor<int32, [3]> reduce_mean_45_axes_0 = const()[name = string("reduce_mean_45_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_45_keep_dims_0 = const()[name = string("reduce_mean_45_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_45_cast_fp16 = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60_cast_fp16)[name = string("reduce_mean_45_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> sub_30_cast_fp16 = sub(x = reshape_60_cast_fp16, y = reduce_mean_45_cast_fp16)[name = string("sub_30_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> square_15_cast_fp16 = square(x = sub_30_cast_fp16)[name = string("square_15_cast_fp16")];
tensor<int32, [3]> reduce_mean_47_axes_0 = const()[name = string("reduce_mean_47_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_47_keep_dims_0 = const()[name = string("reduce_mean_47_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_47_cast_fp16 = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15_cast_fp16)[name = string("reduce_mean_47_cast_fp16")];
fp16 add_30_y_0_to_fp16 = const()[name = string("add_30_y_0_to_fp16"), val = fp16(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_30_cast_fp16 = add(x = reduce_mean_47_cast_fp16, y = add_30_y_0_to_fp16)[name = string("add_30_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_15_cast_fp16 = sqrt(x = add_30_cast_fp16)[name = string("sqrt_15_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> real_div_15_cast_fp16 = real_div(x = sub_30_cast_fp16, y = sqrt_15_cast_fp16)[name = string("real_div_15_cast_fp16")];
tensor<int32, [4]> reshape_61_shape_0 = const()[name = string("reshape_61_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])];
tensor<fp16, [1, 512, 64, 64]> reshape_61_cast_fp16 = reshape(shape = reshape_61_shape_0, x = real_div_15_cast_fp16)[name = string("reshape_61_cast_fp16")];
tensor<fp16, [512]> add_31_gamma_0_to_fp16 = const()[name = string("add_31_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42548416)))];
tensor<fp16, [512]> add_31_beta_0_to_fp16 = const()[name = string("add_31_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42549504)))];
fp16 add_31_epsilon_0_to_fp16 = const()[name = string("add_31_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, 512, 64, 64]> add_31_cast_fp16 = batch_norm(beta = add_31_beta_0_to_fp16, epsilon = add_31_epsilon_0_to_fp16, gamma = add_31_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_61_cast_fp16)[name = string("add_31_cast_fp16")];
tensor<fp16, [1, 512, 64, 64]> input_109_cast_fp16 = silu(x = add_31_cast_fp16)[name = string("input_109_cast_fp16")];
string hidden_states_27_pad_type_0 = const()[name = string("hidden_states_27_pad_type_0"), val = string("custom")];
tensor<int32, [4]> hidden_states_27_pad_0 = const()[name = string("hidden_states_27_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<int32, [2]> hidden_states_27_strides_0 = const()[name = string("hidden_states_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> hidden_states_27_dilations_0 = const()[name = string("hidden_states_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 hidden_states_27_groups_0 = const()[name = string("hidden_states_27_groups_0"), val = int32(1)];
tensor<fp16, [512, 512, 3, 3]> encoder_down_blocks_3_resnets_1_conv2_weight_to_fp16 = const()[name = string("encoder_down_blocks_3_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42550592)))];
tensor<fp16, [512]> encoder_down_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = string("encoder_down_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47269248)))];
tensor<fp16, [1, 512, 64, 64]> hidden_states_27_cast_fp16 = conv(bias = encoder_down_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = hidden_states_27_dilations_0, groups = hidden_states_27_groups_0, pad = hidden_states_27_pad_0, pad_type = hidden_states_27_pad_type_0, strides = hidden_states_27_strides_0, weight = encoder_down_blocks_3_resnets_1_conv2_weight_to_fp16, x = input_109_cast_fp16)[name = string("hidden_states_27_cast_fp16")];
tensor<fp16, [1, 512, 64, 64]> var_343_cast_fp16 = add(x = var_313_cast_fp16, y = hidden_states_27_cast_fp16)[name = string("op_343_cast_fp16")];
tensor<int32, [5]> reshape_64_shape_0 = const()[name = string("reshape_64_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])];
tensor<fp16, [1, 32, 16, 64, 64]> reshape_64_cast_fp16 = reshape(shape = reshape_64_shape_0, x = var_343_cast_fp16)[name = string("reshape_64_cast_fp16")];
tensor<int32, [3]> reduce_mean_48_axes_0 = const()[name = string("reduce_mean_48_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_48_keep_dims_0 = const()[name = string("reduce_mean_48_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_48_cast_fp16 = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64_cast_fp16)[name = string("reduce_mean_48_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> sub_32_cast_fp16 = sub(x = reshape_64_cast_fp16, y = reduce_mean_48_cast_fp16)[name = string("sub_32_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> square_16_cast_fp16 = square(x = sub_32_cast_fp16)[name = string("square_16_cast_fp16")];
tensor<int32, [3]> reduce_mean_50_axes_0 = const()[name = string("reduce_mean_50_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_50_keep_dims_0 = const()[name = string("reduce_mean_50_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_50_cast_fp16 = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16_cast_fp16)[name = string("reduce_mean_50_cast_fp16")];
fp16 add_32_y_0_to_fp16 = const()[name = string("add_32_y_0_to_fp16"), val = fp16(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_32_cast_fp16 = add(x = reduce_mean_50_cast_fp16, y = add_32_y_0_to_fp16)[name = string("add_32_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_16_cast_fp16 = sqrt(x = add_32_cast_fp16)[name = string("sqrt_16_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> real_div_16_cast_fp16 = real_div(x = sub_32_cast_fp16, y = sqrt_16_cast_fp16)[name = string("real_div_16_cast_fp16")];
tensor<int32, [4]> reshape_65_shape_0 = const()[name = string("reshape_65_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])];
tensor<fp16, [1, 512, 64, 64]> reshape_65_cast_fp16 = reshape(shape = reshape_65_shape_0, x = real_div_16_cast_fp16)[name = string("reshape_65_cast_fp16")];
tensor<fp16, [512]> add_33_gamma_0_to_fp16 = const()[name = string("add_33_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47270336)))];
tensor<fp16, [512]> add_33_beta_0_to_fp16 = const()[name = string("add_33_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47271424)))];
fp16 add_33_epsilon_0_to_fp16 = const()[name = string("add_33_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, 512, 64, 64]> add_33_cast_fp16 = batch_norm(beta = add_33_beta_0_to_fp16, epsilon = add_33_epsilon_0_to_fp16, gamma = add_33_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_65_cast_fp16)[name = string("add_33_cast_fp16")];
tensor<fp16, [1, 512, 64, 64]> input_117_cast_fp16 = silu(x = add_33_cast_fp16)[name = string("input_117_cast_fp16")];
string input_119_pad_type_0 = const()[name = string("input_119_pad_type_0"), val = string("custom")];
tensor<int32, [4]> input_119_pad_0 = const()[name = string("input_119_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<int32, [2]> input_119_strides_0 = const()[name = string("input_119_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> input_119_dilations_0 = const()[name = string("input_119_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 input_119_groups_0 = const()[name = string("input_119_groups_0"), val = int32(1)];
tensor<fp16, [512, 512, 3, 3]> encoder_mid_block_resnets_0_conv1_weight_to_fp16 = const()[name = string("encoder_mid_block_resnets_0_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47272512)))];
tensor<fp16, [512]> encoder_mid_block_resnets_0_conv1_bias_to_fp16 = const()[name = string("encoder_mid_block_resnets_0_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51991168)))];
tensor<fp16, [1, 512, 64, 64]> input_119_cast_fp16 = conv(bias = encoder_mid_block_resnets_0_conv1_bias_to_fp16, dilations = input_119_dilations_0, groups = input_119_groups_0, pad = input_119_pad_0, pad_type = input_119_pad_type_0, strides = input_119_strides_0, weight = encoder_mid_block_resnets_0_conv1_weight_to_fp16, x = input_117_cast_fp16)[name = string("input_119_cast_fp16")];
tensor<int32, [5]> reshape_68_shape_0 = const()[name = string("reshape_68_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])];
tensor<fp16, [1, 32, 16, 64, 64]> reshape_68_cast_fp16 = reshape(shape = reshape_68_shape_0, x = input_119_cast_fp16)[name = string("reshape_68_cast_fp16")];
tensor<int32, [3]> reduce_mean_51_axes_0 = const()[name = string("reduce_mean_51_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_51_keep_dims_0 = const()[name = string("reduce_mean_51_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_51_cast_fp16 = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68_cast_fp16)[name = string("reduce_mean_51_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> sub_34_cast_fp16 = sub(x = reshape_68_cast_fp16, y = reduce_mean_51_cast_fp16)[name = string("sub_34_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> square_17_cast_fp16 = square(x = sub_34_cast_fp16)[name = string("square_17_cast_fp16")];
tensor<int32, [3]> reduce_mean_53_axes_0 = const()[name = string("reduce_mean_53_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_53_keep_dims_0 = const()[name = string("reduce_mean_53_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_53_cast_fp16 = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17_cast_fp16)[name = string("reduce_mean_53_cast_fp16")];
fp16 add_34_y_0_to_fp16 = const()[name = string("add_34_y_0_to_fp16"), val = fp16(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_34_cast_fp16 = add(x = reduce_mean_53_cast_fp16, y = add_34_y_0_to_fp16)[name = string("add_34_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_17_cast_fp16 = sqrt(x = add_34_cast_fp16)[name = string("sqrt_17_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> real_div_17_cast_fp16 = real_div(x = sub_34_cast_fp16, y = sqrt_17_cast_fp16)[name = string("real_div_17_cast_fp16")];
tensor<int32, [4]> reshape_69_shape_0 = const()[name = string("reshape_69_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])];
tensor<fp16, [1, 512, 64, 64]> reshape_69_cast_fp16 = reshape(shape = reshape_69_shape_0, x = real_div_17_cast_fp16)[name = string("reshape_69_cast_fp16")];
tensor<fp16, [512]> add_35_gamma_0_to_fp16 = const()[name = string("add_35_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51992256)))];
tensor<fp16, [512]> add_35_beta_0_to_fp16 = const()[name = string("add_35_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51993344)))];
fp16 add_35_epsilon_0_to_fp16 = const()[name = string("add_35_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, 512, 64, 64]> add_35_cast_fp16 = batch_norm(beta = add_35_beta_0_to_fp16, epsilon = add_35_epsilon_0_to_fp16, gamma = add_35_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_69_cast_fp16)[name = string("add_35_cast_fp16")];
tensor<fp16, [1, 512, 64, 64]> input_123_cast_fp16 = silu(x = add_35_cast_fp16)[name = string("input_123_cast_fp16")];
string hidden_states_29_pad_type_0 = const()[name = string("hidden_states_29_pad_type_0"), val = string("custom")];
tensor<int32, [4]> hidden_states_29_pad_0 = const()[name = string("hidden_states_29_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<int32, [2]> hidden_states_29_strides_0 = const()[name = string("hidden_states_29_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> hidden_states_29_dilations_0 = const()[name = string("hidden_states_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 hidden_states_29_groups_0 = const()[name = string("hidden_states_29_groups_0"), val = int32(1)];
tensor<fp16, [512, 512, 3, 3]> encoder_mid_block_resnets_0_conv2_weight_to_fp16 = const()[name = string("encoder_mid_block_resnets_0_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51994432)))];
tensor<fp16, [512]> encoder_mid_block_resnets_0_conv2_bias_to_fp16 = const()[name = string("encoder_mid_block_resnets_0_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56713088)))];
tensor<fp16, [1, 512, 64, 64]> hidden_states_29_cast_fp16 = conv(bias = encoder_mid_block_resnets_0_conv2_bias_to_fp16, dilations = hidden_states_29_dilations_0, groups = hidden_states_29_groups_0, pad = hidden_states_29_pad_0, pad_type = hidden_states_29_pad_type_0, strides = hidden_states_29_strides_0, weight = encoder_mid_block_resnets_0_conv2_weight_to_fp16, x = input_123_cast_fp16)[name = string("hidden_states_29_cast_fp16")];
tensor<fp16, [1, 512, 64, 64]> var_379_cast_fp16 = add(x = var_343_cast_fp16, y = hidden_states_29_cast_fp16)[name = string("op_379_cast_fp16")];
tensor<int32, [4]> reshape_72_shape_0 = const()[name = string("reshape_72_shape_0"), val = tensor<int32, [4]>([1, 32, 16, 4096])];
tensor<fp16, [1, 32, 16, 4096]> reshape_72_cast_fp16 = reshape(shape = reshape_72_shape_0, x = var_379_cast_fp16)[name = string("reshape_72_cast_fp16")];
tensor<int32, [2]> reduce_mean_54_axes_0 = const()[name = string("reduce_mean_54_axes_0"), val = tensor<int32, [2]>([2, 3])];
bool reduce_mean_54_keep_dims_0 = const()[name = string("reduce_mean_54_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1]> reduce_mean_54_cast_fp16 = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72_cast_fp16)[name = string("reduce_mean_54_cast_fp16")];
tensor<fp16, [1, 32, 16, 4096]> sub_36_cast_fp16 = sub(x = reshape_72_cast_fp16, y = reduce_mean_54_cast_fp16)[name = string("sub_36_cast_fp16")];
tensor<fp16, [1, 32, 16, 4096]> square_18_cast_fp16 = square(x = sub_36_cast_fp16)[name = string("square_18_cast_fp16")];
tensor<int32, [2]> reduce_mean_56_axes_0 = const()[name = string("reduce_mean_56_axes_0"), val = tensor<int32, [2]>([2, 3])];
bool reduce_mean_56_keep_dims_0 = const()[name = string("reduce_mean_56_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1]> reduce_mean_56_cast_fp16 = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18_cast_fp16)[name = string("reduce_mean_56_cast_fp16")];
fp16 add_36_y_0_to_fp16 = const()[name = string("add_36_y_0_to_fp16"), val = fp16(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1]> add_36_cast_fp16 = add(x = reduce_mean_56_cast_fp16, y = add_36_y_0_to_fp16)[name = string("add_36_cast_fp16")];
tensor<fp16, [1, 32, 1, 1]> sqrt_18_cast_fp16 = sqrt(x = add_36_cast_fp16)[name = string("sqrt_18_cast_fp16")];
tensor<fp16, [1, 32, 16, 4096]> real_div_18_cast_fp16 = real_div(x = sub_36_cast_fp16, y = sqrt_18_cast_fp16)[name = string("real_div_18_cast_fp16")];
tensor<int32, [3]> reshape_73_shape_0 = const()[name = string("reshape_73_shape_0"), val = tensor<int32, [3]>([1, 512, 4096])];
tensor<fp16, [1, 512, 4096]> reshape_73_cast_fp16 = reshape(shape = reshape_73_shape_0, x = real_div_18_cast_fp16)[name = string("reshape_73_cast_fp16")];
tensor<fp16, [1, 512, 1]> reshape_74_to_fp16 = const()[name = string("reshape_74_to_fp16"), val = tensor<fp16, [1, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56714176)))];
tensor<fp16, [1, 512, 4096]> mul_18_cast_fp16 = mul(x = reshape_73_cast_fp16, y = reshape_74_to_fp16)[name = string("mul_18_cast_fp16")];
tensor<fp16, [1, 512, 1]> reshape_75_to_fp16 = const()[name = string("reshape_75_to_fp16"), val = tensor<fp16, [1, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56715264)))];
tensor<fp16, [1, 512, 4096]> add_37_cast_fp16 = add(x = mul_18_cast_fp16, y = reshape_75_to_fp16)[name = string("add_37_cast_fp16")];
tensor<int32, [3]> input_129_perm_0 = const()[name = string("input_129_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<fp16, [512, 512]> encoder_mid_block_attentions_0_to_q_weight_to_fp16 = const()[name = string("encoder_mid_block_attentions_0_to_q_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56716352)))];
tensor<fp16, [512]> encoder_mid_block_attentions_0_to_q_bias_to_fp16 = const()[name = string("encoder_mid_block_attentions_0_to_q_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57240704)))];
tensor<fp16, [1, 4096, 512]> input_129_cast_fp16 = transpose(perm = input_129_perm_0, x = add_37_cast_fp16)[name = string("transpose_14")];
tensor<fp16, [1, 4096, 512]> linear_0_cast_fp16 = linear(bias = encoder_mid_block_attentions_0_to_q_bias_to_fp16, weight = encoder_mid_block_attentions_0_to_q_weight_to_fp16, x = input_129_cast_fp16)[name = string("linear_0_cast_fp16")];
tensor<fp16, [512, 512]> encoder_mid_block_attentions_0_to_k_weight_to_fp16 = const()[name = string("encoder_mid_block_attentions_0_to_k_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57241792)))];
tensor<fp16, [512]> encoder_mid_block_attentions_0_to_k_bias_to_fp16 = const()[name = string("encoder_mid_block_attentions_0_to_k_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57766144)))];
tensor<fp16, [1, 4096, 512]> linear_1_cast_fp16 = linear(bias = encoder_mid_block_attentions_0_to_k_bias_to_fp16, weight = encoder_mid_block_attentions_0_to_k_weight_to_fp16, x = input_129_cast_fp16)[name = string("linear_1_cast_fp16")];
tensor<fp16, [512, 512]> encoder_mid_block_attentions_0_to_v_weight_to_fp16 = const()[name = string("encoder_mid_block_attentions_0_to_v_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(57767232)))];
tensor<fp16, [512]> encoder_mid_block_attentions_0_to_v_bias_to_fp16 = const()[name = string("encoder_mid_block_attentions_0_to_v_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58291584)))];
tensor<fp16, [1, 4096, 512]> linear_2_cast_fp16 = linear(bias = encoder_mid_block_attentions_0_to_v_bias_to_fp16, weight = encoder_mid_block_attentions_0_to_v_weight_to_fp16, x = input_129_cast_fp16)[name = string("linear_2_cast_fp16")];
tensor<int32, [4]> var_420 = const()[name = string("op_420"), val = tensor<int32, [4]>([1, -1, 1, 512])];
tensor<fp16, [1, 4096, 1, 512]> var_421_cast_fp16 = reshape(shape = var_420, x = linear_0_cast_fp16)[name = string("op_421_cast_fp16")];
tensor<int32, [4]> var_423 = const()[name = string("op_423"), val = tensor<int32, [4]>([1, -1, 1, 512])];
tensor<fp16, [1, 4096, 1, 512]> var_424_cast_fp16 = reshape(shape = var_423, x = linear_1_cast_fp16)[name = string("op_424_cast_fp16")];
tensor<int32, [4]> var_426 = const()[name = string("op_426"), val = tensor<int32, [4]>([1, -1, 1, 512])];
tensor<fp16, [1, 4096, 1, 512]> var_427_cast_fp16 = reshape(shape = var_426, x = linear_2_cast_fp16)[name = string("op_427_cast_fp16")];
tensor<int32, [4]> transpose_6_perm_0 = const()[name = string("transpose_6_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> transpose_7_perm_0 = const()[name = string("transpose_7_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [4]> transpose_8_perm_0 = const()[name = string("transpose_8_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<fp16, [1, 1, 4096, 512]> transpose_8 = transpose(perm = transpose_8_perm_0, x = var_427_cast_fp16)[name = string("transpose_11")];
tensor<fp16, [1, 1, 4096, 512]> transpose_7 = transpose(perm = transpose_7_perm_0, x = var_424_cast_fp16)[name = string("transpose_12")];
tensor<fp16, [1, 1, 4096, 512]> transpose_6 = transpose(perm = transpose_6_perm_0, x = var_421_cast_fp16)[name = string("transpose_13")];
tensor<fp16, [1, 1, 4096, 512]> hidden_states_35_cast_fp16 = scaled_dot_product_attention(key = transpose_7, query = transpose_6, value = transpose_8)[name = string("hidden_states_35_cast_fp16")];
tensor<int32, [4]> var_430_perm_0 = const()[name = string("op_430_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
tensor<int32, [3]> var_434 = const()[name = string("op_434"), val = tensor<int32, [3]>([1, -1, 512])];
tensor<fp16, [1, 4096, 1, 512]> var_430_cast_fp16 = transpose(perm = var_430_perm_0, x = hidden_states_35_cast_fp16)[name = string("transpose_10")];
tensor<fp16, [1, 4096, 512]> hidden_states_37_cast_fp16 = reshape(shape = var_434, x = var_430_cast_fp16)[name = string("hidden_states_37_cast_fp16")];
tensor<fp16, [512, 512]> encoder_mid_block_attentions_0_to_out_0_weight_to_fp16 = const()[name = string("encoder_mid_block_attentions_0_to_out_0_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58292672)))];
tensor<fp16, [512]> encoder_mid_block_attentions_0_to_out_0_bias_to_fp16 = const()[name = string("encoder_mid_block_attentions_0_to_out_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58817024)))];
tensor<fp16, [1, 4096, 512]> linear_3_cast_fp16 = linear(bias = encoder_mid_block_attentions_0_to_out_0_bias_to_fp16, weight = encoder_mid_block_attentions_0_to_out_0_weight_to_fp16, x = hidden_states_37_cast_fp16)[name = string("linear_3_cast_fp16")];
tensor<int32, [3]> var_441_perm_0 = const()[name = string("op_441_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
tensor<int32, [4]> var_442 = const()[name = string("op_442"), val = tensor<int32, [4]>([1, 512, 64, 64])];
tensor<fp16, [1, 512, 4096]> var_441_cast_fp16 = transpose(perm = var_441_perm_0, x = linear_3_cast_fp16)[name = string("transpose_9")];
tensor<fp16, [1, 512, 64, 64]> hidden_states_41_cast_fp16 = reshape(shape = var_442, x = var_441_cast_fp16)[name = string("hidden_states_41_cast_fp16")];
tensor<fp16, [1, 512, 64, 64]> hidden_states_43_cast_fp16 = add(x = hidden_states_41_cast_fp16, y = var_379_cast_fp16)[name = string("hidden_states_43_cast_fp16")];
tensor<int32, [5]> reshape_76_shape_0 = const()[name = string("reshape_76_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])];
tensor<fp16, [1, 32, 16, 64, 64]> reshape_76_cast_fp16 = reshape(shape = reshape_76_shape_0, x = hidden_states_43_cast_fp16)[name = string("reshape_76_cast_fp16")];
tensor<int32, [3]> reduce_mean_57_axes_0 = const()[name = string("reduce_mean_57_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_57_keep_dims_0 = const()[name = string("reduce_mean_57_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_57_cast_fp16 = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76_cast_fp16)[name = string("reduce_mean_57_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> sub_38_cast_fp16 = sub(x = reshape_76_cast_fp16, y = reduce_mean_57_cast_fp16)[name = string("sub_38_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> square_19_cast_fp16 = square(x = sub_38_cast_fp16)[name = string("square_19_cast_fp16")];
tensor<int32, [3]> reduce_mean_59_axes_0 = const()[name = string("reduce_mean_59_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_59_keep_dims_0 = const()[name = string("reduce_mean_59_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_59_cast_fp16 = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19_cast_fp16)[name = string("reduce_mean_59_cast_fp16")];
fp16 add_38_y_0_to_fp16 = const()[name = string("add_38_y_0_to_fp16"), val = fp16(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_38_cast_fp16 = add(x = reduce_mean_59_cast_fp16, y = add_38_y_0_to_fp16)[name = string("add_38_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_19_cast_fp16 = sqrt(x = add_38_cast_fp16)[name = string("sqrt_19_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> real_div_19_cast_fp16 = real_div(x = sub_38_cast_fp16, y = sqrt_19_cast_fp16)[name = string("real_div_19_cast_fp16")];
tensor<int32, [4]> reshape_77_shape_0 = const()[name = string("reshape_77_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])];
tensor<fp16, [1, 512, 64, 64]> reshape_77_cast_fp16 = reshape(shape = reshape_77_shape_0, x = real_div_19_cast_fp16)[name = string("reshape_77_cast_fp16")];
tensor<fp16, [512]> add_39_gamma_0_to_fp16 = const()[name = string("add_39_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58818112)))];
tensor<fp16, [512]> add_39_beta_0_to_fp16 = const()[name = string("add_39_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58819200)))];
fp16 add_39_epsilon_0_to_fp16 = const()[name = string("add_39_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, 512, 64, 64]> add_39_cast_fp16 = batch_norm(beta = add_39_beta_0_to_fp16, epsilon = add_39_epsilon_0_to_fp16, gamma = add_39_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_77_cast_fp16)[name = string("add_39_cast_fp16")];
tensor<fp16, [1, 512, 64, 64]> input_139_cast_fp16 = silu(x = add_39_cast_fp16)[name = string("input_139_cast_fp16")];
string input_141_pad_type_0 = const()[name = string("input_141_pad_type_0"), val = string("custom")];
tensor<int32, [4]> input_141_pad_0 = const()[name = string("input_141_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<int32, [2]> input_141_strides_0 = const()[name = string("input_141_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> input_141_dilations_0 = const()[name = string("input_141_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 input_141_groups_0 = const()[name = string("input_141_groups_0"), val = int32(1)];
tensor<fp16, [512, 512, 3, 3]> encoder_mid_block_resnets_1_conv1_weight_to_fp16 = const()[name = string("encoder_mid_block_resnets_1_conv1_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(58820288)))];
tensor<fp16, [512]> encoder_mid_block_resnets_1_conv1_bias_to_fp16 = const()[name = string("encoder_mid_block_resnets_1_conv1_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63538944)))];
tensor<fp16, [1, 512, 64, 64]> input_141_cast_fp16 = conv(bias = encoder_mid_block_resnets_1_conv1_bias_to_fp16, dilations = input_141_dilations_0, groups = input_141_groups_0, pad = input_141_pad_0, pad_type = input_141_pad_type_0, strides = input_141_strides_0, weight = encoder_mid_block_resnets_1_conv1_weight_to_fp16, x = input_139_cast_fp16)[name = string("input_141_cast_fp16")];
tensor<int32, [5]> reshape_80_shape_0 = const()[name = string("reshape_80_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])];
tensor<fp16, [1, 32, 16, 64, 64]> reshape_80_cast_fp16 = reshape(shape = reshape_80_shape_0, x = input_141_cast_fp16)[name = string("reshape_80_cast_fp16")];
tensor<int32, [3]> reduce_mean_60_axes_0 = const()[name = string("reduce_mean_60_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_60_keep_dims_0 = const()[name = string("reduce_mean_60_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_60_cast_fp16 = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80_cast_fp16)[name = string("reduce_mean_60_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> sub_40_cast_fp16 = sub(x = reshape_80_cast_fp16, y = reduce_mean_60_cast_fp16)[name = string("sub_40_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> square_20_cast_fp16 = square(x = sub_40_cast_fp16)[name = string("square_20_cast_fp16")];
tensor<int32, [3]> reduce_mean_62_axes_0 = const()[name = string("reduce_mean_62_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_62_keep_dims_0 = const()[name = string("reduce_mean_62_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_62_cast_fp16 = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20_cast_fp16)[name = string("reduce_mean_62_cast_fp16")];
fp16 add_40_y_0_to_fp16 = const()[name = string("add_40_y_0_to_fp16"), val = fp16(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_40_cast_fp16 = add(x = reduce_mean_62_cast_fp16, y = add_40_y_0_to_fp16)[name = string("add_40_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_20_cast_fp16 = sqrt(x = add_40_cast_fp16)[name = string("sqrt_20_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> real_div_20_cast_fp16 = real_div(x = sub_40_cast_fp16, y = sqrt_20_cast_fp16)[name = string("real_div_20_cast_fp16")];
tensor<int32, [4]> reshape_81_shape_0 = const()[name = string("reshape_81_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])];
tensor<fp16, [1, 512, 64, 64]> reshape_81_cast_fp16 = reshape(shape = reshape_81_shape_0, x = real_div_20_cast_fp16)[name = string("reshape_81_cast_fp16")];
tensor<fp16, [512]> add_41_gamma_0_to_fp16 = const()[name = string("add_41_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63540032)))];
tensor<fp16, [512]> add_41_beta_0_to_fp16 = const()[name = string("add_41_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63541120)))];
fp16 add_41_epsilon_0_to_fp16 = const()[name = string("add_41_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, 512, 64, 64]> add_41_cast_fp16 = batch_norm(beta = add_41_beta_0_to_fp16, epsilon = add_41_epsilon_0_to_fp16, gamma = add_41_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_81_cast_fp16)[name = string("add_41_cast_fp16")];
tensor<fp16, [1, 512, 64, 64]> input_145_cast_fp16 = silu(x = add_41_cast_fp16)[name = string("input_145_cast_fp16")];
string hidden_states_pad_type_0 = const()[name = string("hidden_states_pad_type_0"), val = string("custom")];
tensor<int32, [4]> hidden_states_pad_0 = const()[name = string("hidden_states_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<int32, [2]> hidden_states_strides_0 = const()[name = string("hidden_states_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> hidden_states_dilations_0 = const()[name = string("hidden_states_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 hidden_states_groups_0 = const()[name = string("hidden_states_groups_0"), val = int32(1)];
tensor<fp16, [512, 512, 3, 3]> encoder_mid_block_resnets_1_conv2_weight_to_fp16 = const()[name = string("encoder_mid_block_resnets_1_conv2_weight_to_fp16"), val = tensor<fp16, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63542208)))];
tensor<fp16, [512]> encoder_mid_block_resnets_1_conv2_bias_to_fp16 = const()[name = string("encoder_mid_block_resnets_1_conv2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68260864)))];
tensor<fp16, [1, 512, 64, 64]> hidden_states_cast_fp16 = conv(bias = encoder_mid_block_resnets_1_conv2_bias_to_fp16, dilations = hidden_states_dilations_0, groups = hidden_states_groups_0, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = hidden_states_strides_0, weight = encoder_mid_block_resnets_1_conv2_weight_to_fp16, x = input_145_cast_fp16)[name = string("hidden_states_cast_fp16")];
tensor<fp16, [1, 512, 64, 64]> var_474_cast_fp16 = add(x = hidden_states_43_cast_fp16, y = hidden_states_cast_fp16)[name = string("op_474_cast_fp16")];
tensor<int32, [5]> reshape_84_shape_0 = const()[name = string("reshape_84_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 64, 64])];
tensor<fp16, [1, 32, 16, 64, 64]> reshape_84_cast_fp16 = reshape(shape = reshape_84_shape_0, x = var_474_cast_fp16)[name = string("reshape_84_cast_fp16")];
tensor<int32, [3]> reduce_mean_63_axes_0 = const()[name = string("reduce_mean_63_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_63_keep_dims_0 = const()[name = string("reduce_mean_63_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_63_cast_fp16 = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84_cast_fp16)[name = string("reduce_mean_63_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> sub_42_cast_fp16 = sub(x = reshape_84_cast_fp16, y = reduce_mean_63_cast_fp16)[name = string("sub_42_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> square_21_cast_fp16 = square(x = sub_42_cast_fp16)[name = string("square_21_cast_fp16")];
tensor<int32, [3]> reduce_mean_65_axes_0 = const()[name = string("reduce_mean_65_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
bool reduce_mean_65_keep_dims_0 = const()[name = string("reduce_mean_65_keep_dims_0"), val = bool(true)];
tensor<fp16, [1, 32, 1, 1, 1]> reduce_mean_65_cast_fp16 = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21_cast_fp16)[name = string("reduce_mean_65_cast_fp16")];
fp16 add_42_y_0_to_fp16 = const()[name = string("add_42_y_0_to_fp16"), val = fp16(0x1.1p-20)];
tensor<fp16, [1, 32, 1, 1, 1]> add_42_cast_fp16 = add(x = reduce_mean_65_cast_fp16, y = add_42_y_0_to_fp16)[name = string("add_42_cast_fp16")];
tensor<fp16, [1, 32, 1, 1, 1]> sqrt_21_cast_fp16 = sqrt(x = add_42_cast_fp16)[name = string("sqrt_21_cast_fp16")];
tensor<fp16, [1, 32, 16, 64, 64]> real_div_21_cast_fp16 = real_div(x = sub_42_cast_fp16, y = sqrt_21_cast_fp16)[name = string("real_div_21_cast_fp16")];
tensor<int32, [4]> reshape_85_shape_0 = const()[name = string("reshape_85_shape_0"), val = tensor<int32, [4]>([1, 512, 64, 64])];
tensor<fp16, [1, 512, 64, 64]> reshape_85_cast_fp16 = reshape(shape = reshape_85_shape_0, x = real_div_21_cast_fp16)[name = string("reshape_85_cast_fp16")];
tensor<fp16, [512]> add_43_gamma_0_to_fp16 = const()[name = string("add_43_gamma_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68261952)))];
tensor<fp16, [512]> add_43_beta_0_to_fp16 = const()[name = string("add_43_beta_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68263040)))];
fp16 add_43_epsilon_0_to_fp16 = const()[name = string("add_43_epsilon_0_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, 512, 64, 64]> add_43_cast_fp16 = batch_norm(beta = add_43_beta_0_to_fp16, epsilon = add_43_epsilon_0_to_fp16, gamma = add_43_gamma_0_to_fp16, mean = add_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_85_cast_fp16)[name = string("add_43_cast_fp16")];
tensor<fp16, [1, 512, 64, 64]> input_153_cast_fp16 = silu(x = add_43_cast_fp16)[name = string("input_153_cast_fp16")];
string input_pad_type_0 = const()[name = string("input_pad_type_0"), val = string("custom")];
tensor<int32, [4]> input_pad_0 = const()[name = string("input_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
tensor<int32, [2]> input_strides_0 = const()[name = string("input_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [2]> input_dilations_0 = const()[name = string("input_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 input_groups_0 = const()[name = string("input_groups_0"), val = int32(1)];
tensor<fp16, [8, 512, 3, 3]> encoder_conv_out_weight_to_fp16 = const()[name = string("encoder_conv_out_weight_to_fp16"), val = tensor<fp16, [8, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68264128)))];
tensor<fp16, [8]> encoder_conv_out_bias_to_fp16 = const()[name = 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, 64, 64]> input_cast_fp16 = conv(bias = encoder_conv_out_bias_to_fp16, dilations = input_dilations_0, groups = input_groups_0, pad = input_pad_0, pad_type = input_pad_type_0, strides = input_strides_0, weight = encoder_conv_out_weight_to_fp16, x = input_153_cast_fp16)[name = string("input_cast_fp16")];
string var_498_pad_type_0 = const()[name = string("op_498_pad_type_0"), val = string("valid")];
tensor<int32, [2]> var_498_strides_0 = const()[name = string("op_498_strides_0"), val = tensor<int32, [2]>([1, 1])];
tensor<int32, [4]> var_498_pad_0 = const()[name = string("op_498_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [2]> var_498_dilations_0 = const()[name = string("op_498_dilations_0"), val = tensor<int32, [2]>([1, 1])];
int32 var_498_groups_0 = const()[name = string("op_498_groups_0"), val = int32(1)];
tensor<fp16, [8, 8, 1, 1]> quant_conv_weight_to_fp16 = const()[name = string("quant_conv_weight_to_fp16"), val = tensor<fp16, [8, 8, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(68337920)))];
tensor<fp16, [8]> quant_conv_bias_to_fp16 = const()[name = 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, 64, 64]> var_498_cast_fp16 = conv(bias = quant_conv_bias_to_fp16, dilations = var_498_dilations_0, groups = var_498_groups_0, pad = var_498_pad_0, pad_type = var_498_pad_type_0, strides = var_498_strides_0, weight = quant_conv_weight_to_fp16, x = input_cast_fp16)[name = string("op_498_cast_fp16")];
string var_498_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_498_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
tensor<fp32, [1, 8, 64, 64]> latent = cast(dtype = var_498_cast_fp16_to_fp32_dtype_0, x = var_498_cast_fp16)[name = string("cast_29")];
} -> (latent);
} |