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[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
{
func main<ios18>(tensor<fp32, [1, 8, 16]> style_dp, tensor<int32, [1, 128]> text_ids, tensor<fp32, [1, 1, 128]> text_mask) {
int32 var_25 = const()[name = string("op_25"), val = int32(2)];
int32 var_26 = const()[name = string("op_26"), val = int32(1)];
int32 var_27 = const()[name = string("op_27"), val = int32(-1)];
int32 x_1_batch_dims_0 = const()[name = string("x_1_batch_dims_0"), val = int32(0)];
bool x_1_validate_indices_0 = const()[name = string("x_1_validate_indices_0"), val = bool(false)];
tensor<fp16, [8322, 64]> m_char_embedder_weight_to_fp16 = const()[name = string("m_char_embedder_weight_to_fp16"), val = tensor<fp16, [8322, 64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
string text_ids_to_int16_dtype_0 = const()[name = string("text_ids_to_int16_dtype_0"), val = string("int16")];
string cast_19_dtype_0 = const()[name = string("cast_19_dtype_0"), val = string("int32")];
int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)];
tensor<int16, [1, 128]> text_ids_to_int16 = cast(dtype = text_ids_to_int16_dtype_0, x = text_ids)[name = string("cast_25")];
tensor<int32, [1, 128]> cast_19 = cast(dtype = cast_19_dtype_0, x = text_ids_to_int16)[name = string("cast_24")];
tensor<bool, [1, 128]> greater_equal_0 = greater_equal(x = cast_19, y = greater_equal_0_y_0)[name = string("greater_equal_0")];
int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(8322)];
tensor<int32, [1, 128]> add_0 = add(x = cast_19, y = slice_by_index_0)[name = string("add_0")];
tensor<int32, [1, 128]> select_0 = select(a = cast_19, b = add_0, cond = greater_equal_0)[name = string("select_0")];
int32 x_1_cast_fp16_cast_uint16_axis_0 = const()[name = string("x_1_cast_fp16_cast_uint16_axis_0"), val = int32(0)];
string select_0_to_int16_dtype_0 = const()[name = string("select_0_to_int16_dtype_0"), val = string("int16")];
tensor<int16, [1, 128]> select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = string("cast_23")];
tensor<fp16, [1, 128, 64]> x_1_cast_fp16_cast_uint16_cast_uint16 = gather(axis = x_1_cast_fp16_cast_uint16_axis_0, batch_dims = x_1_batch_dims_0, indices = select_0_to_int16, validate_indices = x_1_validate_indices_0, x = m_char_embedder_weight_to_fp16)[name = string("x_1_cast_fp16_cast_uint16_cast_uint16")];
tensor<int32, [3]> var_53_perm_0 = const()[name = string("op_53_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
string text_mask_to_fp16_dtype_0 = const()[name = string("text_mask_to_fp16_dtype_0"), val = string("fp16")];
tensor<fp16, [1, 1, 128]> text_mask_to_fp16 = cast(dtype = text_mask_to_fp16_dtype_0, x = text_mask)[name = string("cast_22")];
tensor<fp16, [1, 64, 128]> var_53_cast_fp16 = transpose(perm = var_53_perm_0, x = x_1_cast_fp16_cast_uint16_cast_uint16)[name = string("transpose_24")];
tensor<fp16, [1, 64, 128]> x_3_cast_fp16 = mul(x = var_53_cast_fp16, y = text_mask_to_fp16)[name = string("x_3_cast_fp16")];
bool x_5_interleave_0 = const()[name = string("x_5_interleave_0"), val = bool(false)];
tensor<fp16, [1, 64, 1]> m_sentence_token_to_fp16 = const()[name = string("m_sentence_token_to_fp16"), val = tensor<fp16, [1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1065344)))];
tensor<fp16, [1, 64, 129]> x_5_cast_fp16 = concat(axis = var_25, interleave = x_5_interleave_0, values = (m_sentence_token_to_fp16, x_3_cast_fp16))[name = string("x_5_cast_fp16")];
bool mask_interleave_0 = const()[name = string("mask_interleave_0"), val = bool(false)];
tensor<fp16, [1, 1, 1]> fill_0_to_fp16 = const()[name = string("fill_0_to_fp16"), val = tensor<fp16, [1, 1, 1]>([[[0x1p+0]]])];
tensor<fp16, [1, 1, 129]> mask_cast_fp16 = concat(axis = var_25, interleave = mask_interleave_0, values = (fill_0_to_fp16, text_mask_to_fp16))[name = string("mask_cast_fp16")];
tensor<fp16, [1, 64, 129]> input_1_cast_fp16 = mul(x = x_5_cast_fp16, y = mask_cast_fp16)[name = string("input_1_cast_fp16")];
tensor<int32, [6]> input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
string input_3_mode_0 = const()[name = string("input_3_mode_0"), val = string("replicate")];
fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 64, 133]> input_3_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = input_3_mode_0, pad = input_3_pad_0, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")];
string h_1_pad_type_0 = const()[name = string("h_1_pad_type_0"), val = string("valid")];
int32 h_1_groups_0 = const()[name = string("h_1_groups_0"), val = int32(64)];
tensor<int32, [1]> h_1_strides_0 = const()[name = string("h_1_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> h_1_pad_0 = const()[name = string("h_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> h_1_dilations_0 = const()[name = string("h_1_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<fp16, [64, 1, 5]> m_convnext_0_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_0_dwconv__conv_weight_to_fp16"), val = tensor<fp16, [64, 1, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1065536)))];
tensor<fp16, [64]> m_convnext_0_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_0_dwconv__conv_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1066240)))];
tensor<fp16, [1, 64, 129]> h_1_cast_fp16 = conv(bias = m_convnext_0_dwconv__conv_bias_to_fp16, dilations = h_1_dilations_0, groups = h_1_groups_0, pad = h_1_pad_0, pad_type = h_1_pad_type_0, strides = h_1_strides_0, weight = m_convnext_0_dwconv__conv_weight_to_fp16, x = input_3_cast_fp16)[name = string("h_1_cast_fp16")];
tensor<fp16, [1, 64, 129]> x_7_cast_fp16 = mul(x = h_1_cast_fp16, y = mask_cast_fp16)[name = string("x_7_cast_fp16")];
tensor<int32, [3]> input_5_perm_0 = const()[name = string("input_5_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<int32, [1]> var_85_axes_0 = const()[name = string("op_85_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [64]> m_convnext_0_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_0_norm_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1066432)))];
tensor<fp16, [64]> m_convnext_0_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_0_norm_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1066624)))];
fp16 var_20_to_fp16 = const()[name = string("op_20_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, 129, 64]> input_5_cast_fp16 = transpose(perm = input_5_perm_0, x = x_7_cast_fp16)[name = string("transpose_23")];
tensor<fp16, [1, 129, 64]> var_85_cast_fp16 = layer_norm(axes = var_85_axes_0, beta = m_convnext_0_norm_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_convnext_0_norm_norm_weight_to_fp16, x = input_5_cast_fp16)[name = string("op_85_cast_fp16")];
tensor<int32, [3]> input_7_perm_0 = const()[name = string("input_7_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
string h_3_pad_type_0 = const()[name = string("h_3_pad_type_0"), val = string("valid")];
tensor<int32, [1]> h_3_strides_0 = const()[name = string("h_3_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> h_3_pad_0 = const()[name = string("h_3_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> h_3_dilations_0 = const()[name = string("h_3_dilations_0"), val = tensor<int32, [1]>([1])];
int32 h_3_groups_0 = const()[name = string("h_3_groups_0"), val = int32(1)];
tensor<fp16, [256, 64, 1]> m_convnext_0_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_0_pwconv1_weight_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1066816)))];
tensor<fp16, [256]> m_convnext_0_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_0_pwconv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1099648)))];
tensor<fp16, [1, 64, 129]> input_7_cast_fp16 = transpose(perm = input_7_perm_0, x = var_85_cast_fp16)[name = string("transpose_22")];
tensor<fp16, [1, 256, 129]> h_3_cast_fp16 = conv(bias = m_convnext_0_pwconv1_bias_to_fp16, dilations = h_3_dilations_0, groups = h_3_groups_0, pad = h_3_pad_0, pad_type = h_3_pad_type_0, strides = h_3_strides_0, weight = m_convnext_0_pwconv1_weight_to_fp16, x = input_7_cast_fp16)[name = string("h_3_cast_fp16")];
string input_9_mode_0 = const()[name = string("input_9_mode_0"), val = string("EXACT")];
tensor<fp16, [1, 256, 129]> input_9_cast_fp16 = gelu(mode = input_9_mode_0, x = h_3_cast_fp16)[name = string("input_9_cast_fp16")];
string h_5_pad_type_0 = const()[name = string("h_5_pad_type_0"), val = string("valid")];
tensor<int32, [1]> h_5_strides_0 = const()[name = string("h_5_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> h_5_pad_0 = const()[name = string("h_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> h_5_dilations_0 = const()[name = string("h_5_dilations_0"), val = tensor<int32, [1]>([1])];
int32 h_5_groups_0 = const()[name = string("h_5_groups_0"), val = int32(1)];
tensor<fp16, [64, 256, 1]> var_102_weight_0_to_fp16 = const()[name = string("op_102_weight_0_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1100224)))];
tensor<fp16, [64]> var_102_bias_0_to_fp16 = const()[name = string("op_102_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1133056)))];
tensor<fp16, [1, 64, 129]> var_102_cast_fp16 = conv(bias = var_102_bias_0_to_fp16, dilations = h_5_dilations_0, groups = h_5_groups_0, pad = h_5_pad_0, pad_type = h_5_pad_type_0, strides = h_5_strides_0, weight = var_102_weight_0_to_fp16, x = input_9_cast_fp16)[name = string("op_102_cast_fp16")];
tensor<fp16, [1, 64, 129]> out_1_cast_fp16 = add(x = input_1_cast_fp16, y = var_102_cast_fp16)[name = string("out_1_cast_fp16")];
tensor<fp16, [1, 64, 129]> x_9_cast_fp16 = mul(x = out_1_cast_fp16, y = mask_cast_fp16)[name = string("x_9_cast_fp16")];
tensor<fp16, [1, 64, 129]> input_11_cast_fp16 = mul(x = x_9_cast_fp16, y = mask_cast_fp16)[name = string("input_11_cast_fp16")];
tensor<int32, [6]> input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
string input_13_mode_0 = const()[name = string("input_13_mode_0"), val = string("replicate")];
fp16 const_2_to_fp16 = const()[name = string("const_2_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 64, 133]> input_13_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = input_13_mode_0, pad = input_13_pad_0, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")];
string h_7_pad_type_0 = const()[name = string("h_7_pad_type_0"), val = string("valid")];
int32 h_7_groups_0 = const()[name = string("h_7_groups_0"), val = int32(64)];
tensor<int32, [1]> h_7_strides_0 = const()[name = string("h_7_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> h_7_pad_0 = const()[name = string("h_7_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> h_7_dilations_0 = const()[name = string("h_7_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<fp16, [64, 1, 5]> m_convnext_1_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_1_dwconv__conv_weight_to_fp16"), val = tensor<fp16, [64, 1, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1133248)))];
tensor<fp16, [64]> m_convnext_1_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_1_dwconv__conv_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1133952)))];
tensor<fp16, [1, 64, 129]> h_7_cast_fp16 = conv(bias = m_convnext_1_dwconv__conv_bias_to_fp16, dilations = h_7_dilations_0, groups = h_7_groups_0, pad = h_7_pad_0, pad_type = h_7_pad_type_0, strides = h_7_strides_0, weight = m_convnext_1_dwconv__conv_weight_to_fp16, x = input_13_cast_fp16)[name = string("h_7_cast_fp16")];
tensor<fp16, [1, 64, 129]> x_11_cast_fp16 = mul(x = h_7_cast_fp16, y = mask_cast_fp16)[name = string("x_11_cast_fp16")];
tensor<int32, [3]> input_15_perm_0 = const()[name = string("input_15_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<int32, [1]> var_127_axes_0 = const()[name = string("op_127_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [64]> m_convnext_1_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_1_norm_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1134144)))];
tensor<fp16, [64]> m_convnext_1_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_1_norm_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1134336)))];
tensor<fp16, [1, 129, 64]> input_15_cast_fp16 = transpose(perm = input_15_perm_0, x = x_11_cast_fp16)[name = string("transpose_21")];
tensor<fp16, [1, 129, 64]> var_127_cast_fp16 = layer_norm(axes = var_127_axes_0, beta = m_convnext_1_norm_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_convnext_1_norm_norm_weight_to_fp16, x = input_15_cast_fp16)[name = string("op_127_cast_fp16")];
tensor<int32, [3]> input_17_perm_0 = const()[name = string("input_17_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
string h_9_pad_type_0 = const()[name = string("h_9_pad_type_0"), val = string("valid")];
tensor<int32, [1]> h_9_strides_0 = const()[name = string("h_9_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> h_9_pad_0 = const()[name = string("h_9_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> h_9_dilations_0 = const()[name = string("h_9_dilations_0"), val = tensor<int32, [1]>([1])];
int32 h_9_groups_0 = const()[name = string("h_9_groups_0"), val = int32(1)];
tensor<fp16, [256, 64, 1]> m_convnext_1_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_1_pwconv1_weight_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1134528)))];
tensor<fp16, [256]> m_convnext_1_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_1_pwconv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1167360)))];
tensor<fp16, [1, 64, 129]> input_17_cast_fp16 = transpose(perm = input_17_perm_0, x = var_127_cast_fp16)[name = string("transpose_20")];
tensor<fp16, [1, 256, 129]> h_9_cast_fp16 = conv(bias = m_convnext_1_pwconv1_bias_to_fp16, dilations = h_9_dilations_0, groups = h_9_groups_0, pad = h_9_pad_0, pad_type = h_9_pad_type_0, strides = h_9_strides_0, weight = m_convnext_1_pwconv1_weight_to_fp16, x = input_17_cast_fp16)[name = string("h_9_cast_fp16")];
string input_19_mode_0 = const()[name = string("input_19_mode_0"), val = string("EXACT")];
tensor<fp16, [1, 256, 129]> input_19_cast_fp16 = gelu(mode = input_19_mode_0, x = h_9_cast_fp16)[name = string("input_19_cast_fp16")];
string h_11_pad_type_0 = const()[name = string("h_11_pad_type_0"), val = string("valid")];
tensor<int32, [1]> h_11_strides_0 = const()[name = string("h_11_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> h_11_pad_0 = const()[name = string("h_11_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> h_11_dilations_0 = const()[name = string("h_11_dilations_0"), val = tensor<int32, [1]>([1])];
int32 h_11_groups_0 = const()[name = string("h_11_groups_0"), val = int32(1)];
tensor<fp16, [64, 256, 1]> var_144_weight_0_to_fp16 = const()[name = string("op_144_weight_0_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1167936)))];
tensor<fp16, [64]> var_144_bias_0_to_fp16 = const()[name = string("op_144_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1200768)))];
tensor<fp16, [1, 64, 129]> var_144_cast_fp16 = conv(bias = var_144_bias_0_to_fp16, dilations = h_11_dilations_0, groups = h_11_groups_0, pad = h_11_pad_0, pad_type = h_11_pad_type_0, strides = h_11_strides_0, weight = var_144_weight_0_to_fp16, x = input_19_cast_fp16)[name = string("op_144_cast_fp16")];
tensor<fp16, [1, 64, 129]> out_3_cast_fp16 = add(x = input_11_cast_fp16, y = var_144_cast_fp16)[name = string("out_3_cast_fp16")];
tensor<fp16, [1, 64, 129]> x_13_cast_fp16 = mul(x = out_3_cast_fp16, y = mask_cast_fp16)[name = string("x_13_cast_fp16")];
tensor<fp16, [1, 64, 129]> input_21_cast_fp16 = mul(x = x_13_cast_fp16, y = mask_cast_fp16)[name = string("input_21_cast_fp16")];
tensor<int32, [6]> input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
string input_23_mode_0 = const()[name = string("input_23_mode_0"), val = string("replicate")];
fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 64, 133]> input_23_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = input_23_mode_0, pad = input_23_pad_0, x = input_21_cast_fp16)[name = string("input_23_cast_fp16")];
string h_13_pad_type_0 = const()[name = string("h_13_pad_type_0"), val = string("valid")];
int32 h_13_groups_0 = const()[name = string("h_13_groups_0"), val = int32(64)];
tensor<int32, [1]> h_13_strides_0 = const()[name = string("h_13_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> h_13_pad_0 = const()[name = string("h_13_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> h_13_dilations_0 = const()[name = string("h_13_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<fp16, [64, 1, 5]> m_convnext_2_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_2_dwconv__conv_weight_to_fp16"), val = tensor<fp16, [64, 1, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1200960)))];
tensor<fp16, [64]> m_convnext_2_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_2_dwconv__conv_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1201664)))];
tensor<fp16, [1, 64, 129]> h_13_cast_fp16 = conv(bias = m_convnext_2_dwconv__conv_bias_to_fp16, dilations = h_13_dilations_0, groups = h_13_groups_0, pad = h_13_pad_0, pad_type = h_13_pad_type_0, strides = h_13_strides_0, weight = m_convnext_2_dwconv__conv_weight_to_fp16, x = input_23_cast_fp16)[name = string("h_13_cast_fp16")];
tensor<fp16, [1, 64, 129]> x_15_cast_fp16 = mul(x = h_13_cast_fp16, y = mask_cast_fp16)[name = string("x_15_cast_fp16")];
tensor<int32, [3]> input_25_perm_0 = const()[name = string("input_25_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<int32, [1]> var_169_axes_0 = const()[name = string("op_169_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [64]> m_convnext_2_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_2_norm_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1201856)))];
tensor<fp16, [64]> m_convnext_2_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_2_norm_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1202048)))];
tensor<fp16, [1, 129, 64]> input_25_cast_fp16 = transpose(perm = input_25_perm_0, x = x_15_cast_fp16)[name = string("transpose_19")];
tensor<fp16, [1, 129, 64]> var_169_cast_fp16 = layer_norm(axes = var_169_axes_0, beta = m_convnext_2_norm_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_convnext_2_norm_norm_weight_to_fp16, x = input_25_cast_fp16)[name = string("op_169_cast_fp16")];
tensor<int32, [3]> input_27_perm_0 = const()[name = string("input_27_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
string h_15_pad_type_0 = const()[name = string("h_15_pad_type_0"), val = string("valid")];
tensor<int32, [1]> h_15_strides_0 = const()[name = string("h_15_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> h_15_pad_0 = const()[name = string("h_15_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> h_15_dilations_0 = const()[name = string("h_15_dilations_0"), val = tensor<int32, [1]>([1])];
int32 h_15_groups_0 = const()[name = string("h_15_groups_0"), val = int32(1)];
tensor<fp16, [256, 64, 1]> m_convnext_2_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_2_pwconv1_weight_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1202240)))];
tensor<fp16, [256]> m_convnext_2_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_2_pwconv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1235072)))];
tensor<fp16, [1, 64, 129]> input_27_cast_fp16 = transpose(perm = input_27_perm_0, x = var_169_cast_fp16)[name = string("transpose_18")];
tensor<fp16, [1, 256, 129]> h_15_cast_fp16 = conv(bias = m_convnext_2_pwconv1_bias_to_fp16, dilations = h_15_dilations_0, groups = h_15_groups_0, pad = h_15_pad_0, pad_type = h_15_pad_type_0, strides = h_15_strides_0, weight = m_convnext_2_pwconv1_weight_to_fp16, x = input_27_cast_fp16)[name = string("h_15_cast_fp16")];
string input_29_mode_0 = const()[name = string("input_29_mode_0"), val = string("EXACT")];
tensor<fp16, [1, 256, 129]> input_29_cast_fp16 = gelu(mode = input_29_mode_0, x = h_15_cast_fp16)[name = string("input_29_cast_fp16")];
string h_17_pad_type_0 = const()[name = string("h_17_pad_type_0"), val = string("valid")];
tensor<int32, [1]> h_17_strides_0 = const()[name = string("h_17_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> h_17_pad_0 = const()[name = string("h_17_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> h_17_dilations_0 = const()[name = string("h_17_dilations_0"), val = tensor<int32, [1]>([1])];
int32 h_17_groups_0 = const()[name = string("h_17_groups_0"), val = int32(1)];
tensor<fp16, [64, 256, 1]> var_186_weight_0_to_fp16 = const()[name = string("op_186_weight_0_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1235648)))];
tensor<fp16, [64]> var_186_bias_0_to_fp16 = const()[name = string("op_186_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1268480)))];
tensor<fp16, [1, 64, 129]> var_186_cast_fp16 = conv(bias = var_186_bias_0_to_fp16, dilations = h_17_dilations_0, groups = h_17_groups_0, pad = h_17_pad_0, pad_type = h_17_pad_type_0, strides = h_17_strides_0, weight = var_186_weight_0_to_fp16, x = input_29_cast_fp16)[name = string("op_186_cast_fp16")];
tensor<fp16, [1, 64, 129]> out_5_cast_fp16 = add(x = input_21_cast_fp16, y = var_186_cast_fp16)[name = string("out_5_cast_fp16")];
tensor<fp16, [1, 64, 129]> x_17_cast_fp16 = mul(x = out_5_cast_fp16, y = mask_cast_fp16)[name = string("x_17_cast_fp16")];
tensor<fp16, [1, 64, 129]> input_31_cast_fp16 = mul(x = x_17_cast_fp16, y = mask_cast_fp16)[name = string("input_31_cast_fp16")];
tensor<int32, [6]> input_33_pad_0 = const()[name = string("input_33_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
string input_33_mode_0 = const()[name = string("input_33_mode_0"), val = string("replicate")];
fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 64, 133]> input_33_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = input_33_mode_0, pad = input_33_pad_0, x = input_31_cast_fp16)[name = string("input_33_cast_fp16")];
string h_19_pad_type_0 = const()[name = string("h_19_pad_type_0"), val = string("valid")];
int32 h_19_groups_0 = const()[name = string("h_19_groups_0"), val = int32(64)];
tensor<int32, [1]> h_19_strides_0 = const()[name = string("h_19_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> h_19_pad_0 = const()[name = string("h_19_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> h_19_dilations_0 = const()[name = string("h_19_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<fp16, [64, 1, 5]> m_convnext_3_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_3_dwconv__conv_weight_to_fp16"), val = tensor<fp16, [64, 1, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1268672)))];
tensor<fp16, [64]> m_convnext_3_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_3_dwconv__conv_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1269376)))];
tensor<fp16, [1, 64, 129]> h_19_cast_fp16 = conv(bias = m_convnext_3_dwconv__conv_bias_to_fp16, dilations = h_19_dilations_0, groups = h_19_groups_0, pad = h_19_pad_0, pad_type = h_19_pad_type_0, strides = h_19_strides_0, weight = m_convnext_3_dwconv__conv_weight_to_fp16, x = input_33_cast_fp16)[name = string("h_19_cast_fp16")];
tensor<fp16, [1, 64, 129]> x_19_cast_fp16 = mul(x = h_19_cast_fp16, y = mask_cast_fp16)[name = string("x_19_cast_fp16")];
tensor<int32, [3]> input_35_perm_0 = const()[name = string("input_35_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<int32, [1]> var_211_axes_0 = const()[name = string("op_211_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [64]> m_convnext_3_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_3_norm_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1269568)))];
tensor<fp16, [64]> m_convnext_3_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_3_norm_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1269760)))];
tensor<fp16, [1, 129, 64]> input_35_cast_fp16 = transpose(perm = input_35_perm_0, x = x_19_cast_fp16)[name = string("transpose_17")];
tensor<fp16, [1, 129, 64]> var_211_cast_fp16 = layer_norm(axes = var_211_axes_0, beta = m_convnext_3_norm_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_convnext_3_norm_norm_weight_to_fp16, x = input_35_cast_fp16)[name = string("op_211_cast_fp16")];
tensor<int32, [3]> input_37_perm_0 = const()[name = string("input_37_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
string h_21_pad_type_0 = const()[name = string("h_21_pad_type_0"), val = string("valid")];
tensor<int32, [1]> h_21_strides_0 = const()[name = string("h_21_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> h_21_pad_0 = const()[name = string("h_21_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> h_21_dilations_0 = const()[name = string("h_21_dilations_0"), val = tensor<int32, [1]>([1])];
int32 h_21_groups_0 = const()[name = string("h_21_groups_0"), val = int32(1)];
tensor<fp16, [256, 64, 1]> m_convnext_3_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_3_pwconv1_weight_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1269952)))];
tensor<fp16, [256]> m_convnext_3_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_3_pwconv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1302784)))];
tensor<fp16, [1, 64, 129]> input_37_cast_fp16 = transpose(perm = input_37_perm_0, x = var_211_cast_fp16)[name = string("transpose_16")];
tensor<fp16, [1, 256, 129]> h_21_cast_fp16 = conv(bias = m_convnext_3_pwconv1_bias_to_fp16, dilations = h_21_dilations_0, groups = h_21_groups_0, pad = h_21_pad_0, pad_type = h_21_pad_type_0, strides = h_21_strides_0, weight = m_convnext_3_pwconv1_weight_to_fp16, x = input_37_cast_fp16)[name = string("h_21_cast_fp16")];
string input_39_mode_0 = const()[name = string("input_39_mode_0"), val = string("EXACT")];
tensor<fp16, [1, 256, 129]> input_39_cast_fp16 = gelu(mode = input_39_mode_0, x = h_21_cast_fp16)[name = string("input_39_cast_fp16")];
string h_23_pad_type_0 = const()[name = string("h_23_pad_type_0"), val = string("valid")];
tensor<int32, [1]> h_23_strides_0 = const()[name = string("h_23_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> h_23_pad_0 = const()[name = string("h_23_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> h_23_dilations_0 = const()[name = string("h_23_dilations_0"), val = tensor<int32, [1]>([1])];
int32 h_23_groups_0 = const()[name = string("h_23_groups_0"), val = int32(1)];
tensor<fp16, [64, 256, 1]> var_228_weight_0_to_fp16 = const()[name = string("op_228_weight_0_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1303360)))];
tensor<fp16, [64]> var_228_bias_0_to_fp16 = const()[name = string("op_228_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1336192)))];
tensor<fp16, [1, 64, 129]> var_228_cast_fp16 = conv(bias = var_228_bias_0_to_fp16, dilations = h_23_dilations_0, groups = h_23_groups_0, pad = h_23_pad_0, pad_type = h_23_pad_type_0, strides = h_23_strides_0, weight = var_228_weight_0_to_fp16, x = input_39_cast_fp16)[name = string("op_228_cast_fp16")];
tensor<fp16, [1, 64, 129]> out_7_cast_fp16 = add(x = input_31_cast_fp16, y = var_228_cast_fp16)[name = string("out_7_cast_fp16")];
tensor<fp16, [1, 64, 129]> x_21_cast_fp16 = mul(x = out_7_cast_fp16, y = mask_cast_fp16)[name = string("x_21_cast_fp16")];
tensor<fp16, [1, 64, 129]> input_41_cast_fp16 = mul(x = x_21_cast_fp16, y = mask_cast_fp16)[name = string("input_41_cast_fp16")];
tensor<int32, [6]> input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
string input_43_mode_0 = const()[name = string("input_43_mode_0"), val = string("replicate")];
fp16 const_5_to_fp16 = const()[name = string("const_5_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 64, 133]> input_43_cast_fp16 = pad(constant_val = const_5_to_fp16, mode = input_43_mode_0, pad = input_43_pad_0, x = input_41_cast_fp16)[name = string("input_43_cast_fp16")];
string h_25_pad_type_0 = const()[name = string("h_25_pad_type_0"), val = string("valid")];
int32 h_25_groups_0 = const()[name = string("h_25_groups_0"), val = int32(64)];
tensor<int32, [1]> h_25_strides_0 = const()[name = string("h_25_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> h_25_pad_0 = const()[name = string("h_25_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> h_25_dilations_0 = const()[name = string("h_25_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<fp16, [64, 1, 5]> m_convnext_4_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_4_dwconv__conv_weight_to_fp16"), val = tensor<fp16, [64, 1, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1336384)))];
tensor<fp16, [64]> m_convnext_4_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_4_dwconv__conv_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1337088)))];
tensor<fp16, [1, 64, 129]> h_25_cast_fp16 = conv(bias = m_convnext_4_dwconv__conv_bias_to_fp16, dilations = h_25_dilations_0, groups = h_25_groups_0, pad = h_25_pad_0, pad_type = h_25_pad_type_0, strides = h_25_strides_0, weight = m_convnext_4_dwconv__conv_weight_to_fp16, x = input_43_cast_fp16)[name = string("h_25_cast_fp16")];
tensor<fp16, [1, 64, 129]> x_23_cast_fp16 = mul(x = h_25_cast_fp16, y = mask_cast_fp16)[name = string("x_23_cast_fp16")];
tensor<int32, [3]> input_45_perm_0 = const()[name = string("input_45_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<int32, [1]> var_253_axes_0 = const()[name = string("op_253_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [64]> m_convnext_4_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_4_norm_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1337280)))];
tensor<fp16, [64]> m_convnext_4_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_4_norm_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1337472)))];
tensor<fp16, [1, 129, 64]> input_45_cast_fp16 = transpose(perm = input_45_perm_0, x = x_23_cast_fp16)[name = string("transpose_15")];
tensor<fp16, [1, 129, 64]> var_253_cast_fp16 = layer_norm(axes = var_253_axes_0, beta = m_convnext_4_norm_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_convnext_4_norm_norm_weight_to_fp16, x = input_45_cast_fp16)[name = string("op_253_cast_fp16")];
tensor<int32, [3]> input_47_perm_0 = const()[name = string("input_47_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
string h_27_pad_type_0 = const()[name = string("h_27_pad_type_0"), val = string("valid")];
tensor<int32, [1]> h_27_strides_0 = const()[name = string("h_27_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> h_27_pad_0 = const()[name = string("h_27_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> h_27_dilations_0 = const()[name = string("h_27_dilations_0"), val = tensor<int32, [1]>([1])];
int32 h_27_groups_0 = const()[name = string("h_27_groups_0"), val = int32(1)];
tensor<fp16, [256, 64, 1]> m_convnext_4_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_4_pwconv1_weight_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1337664)))];
tensor<fp16, [256]> m_convnext_4_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_4_pwconv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1370496)))];
tensor<fp16, [1, 64, 129]> input_47_cast_fp16 = transpose(perm = input_47_perm_0, x = var_253_cast_fp16)[name = string("transpose_14")];
tensor<fp16, [1, 256, 129]> h_27_cast_fp16 = conv(bias = m_convnext_4_pwconv1_bias_to_fp16, dilations = h_27_dilations_0, groups = h_27_groups_0, pad = h_27_pad_0, pad_type = h_27_pad_type_0, strides = h_27_strides_0, weight = m_convnext_4_pwconv1_weight_to_fp16, x = input_47_cast_fp16)[name = string("h_27_cast_fp16")];
string input_49_mode_0 = const()[name = string("input_49_mode_0"), val = string("EXACT")];
tensor<fp16, [1, 256, 129]> input_49_cast_fp16 = gelu(mode = input_49_mode_0, x = h_27_cast_fp16)[name = string("input_49_cast_fp16")];
string h_29_pad_type_0 = const()[name = string("h_29_pad_type_0"), val = string("valid")];
tensor<int32, [1]> h_29_strides_0 = const()[name = string("h_29_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> h_29_pad_0 = const()[name = string("h_29_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> h_29_dilations_0 = const()[name = string("h_29_dilations_0"), val = tensor<int32, [1]>([1])];
int32 h_29_groups_0 = const()[name = string("h_29_groups_0"), val = int32(1)];
tensor<fp16, [64, 256, 1]> var_270_weight_0_to_fp16 = const()[name = string("op_270_weight_0_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1371072)))];
tensor<fp16, [64]> var_270_bias_0_to_fp16 = const()[name = string("op_270_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1403904)))];
tensor<fp16, [1, 64, 129]> var_270_cast_fp16 = conv(bias = var_270_bias_0_to_fp16, dilations = h_29_dilations_0, groups = h_29_groups_0, pad = h_29_pad_0, pad_type = h_29_pad_type_0, strides = h_29_strides_0, weight = var_270_weight_0_to_fp16, x = input_49_cast_fp16)[name = string("op_270_cast_fp16")];
tensor<fp16, [1, 64, 129]> out_9_cast_fp16 = add(x = input_41_cast_fp16, y = var_270_cast_fp16)[name = string("out_9_cast_fp16")];
tensor<fp16, [1, 64, 129]> x_25_cast_fp16 = mul(x = out_9_cast_fp16, y = mask_cast_fp16)[name = string("x_25_cast_fp16")];
tensor<fp16, [1, 64, 129]> input_51_cast_fp16 = mul(x = x_25_cast_fp16, y = mask_cast_fp16)[name = string("input_51_cast_fp16")];
tensor<int32, [6]> input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 2])];
string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("replicate")];
fp16 const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 64, 133]> input_53_cast_fp16 = pad(constant_val = const_6_to_fp16, mode = input_53_mode_0, pad = input_53_pad_0, x = input_51_cast_fp16)[name = string("input_53_cast_fp16")];
string h_31_pad_type_0 = const()[name = string("h_31_pad_type_0"), val = string("valid")];
int32 h_31_groups_0 = const()[name = string("h_31_groups_0"), val = int32(64)];
tensor<int32, [1]> h_31_strides_0 = const()[name = string("h_31_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> h_31_pad_0 = const()[name = string("h_31_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> h_31_dilations_0 = const()[name = string("h_31_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<fp16, [64, 1, 5]> m_convnext_5_dwconv__conv_weight_to_fp16 = const()[name = string("m_convnext_5_dwconv__conv_weight_to_fp16"), val = tensor<fp16, [64, 1, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1404096)))];
tensor<fp16, [64]> m_convnext_5_dwconv__conv_bias_to_fp16 = const()[name = string("m_convnext_5_dwconv__conv_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1404800)))];
tensor<fp16, [1, 64, 129]> h_31_cast_fp16 = conv(bias = m_convnext_5_dwconv__conv_bias_to_fp16, dilations = h_31_dilations_0, groups = h_31_groups_0, pad = h_31_pad_0, pad_type = h_31_pad_type_0, strides = h_31_strides_0, weight = m_convnext_5_dwconv__conv_weight_to_fp16, x = input_53_cast_fp16)[name = string("h_31_cast_fp16")];
tensor<fp16, [1, 64, 129]> x_27_cast_fp16 = mul(x = h_31_cast_fp16, y = mask_cast_fp16)[name = string("x_27_cast_fp16")];
tensor<int32, [3]> input_55_perm_0 = const()[name = string("input_55_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<int32, [1]> var_295_axes_0 = const()[name = string("op_295_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [64]> m_convnext_5_norm_norm_weight_to_fp16 = const()[name = string("m_convnext_5_norm_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1404992)))];
tensor<fp16, [64]> m_convnext_5_norm_norm_bias_to_fp16 = const()[name = string("m_convnext_5_norm_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1405184)))];
tensor<fp16, [1, 129, 64]> input_55_cast_fp16 = transpose(perm = input_55_perm_0, x = x_27_cast_fp16)[name = string("transpose_13")];
tensor<fp16, [1, 129, 64]> var_295_cast_fp16 = layer_norm(axes = var_295_axes_0, beta = m_convnext_5_norm_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_convnext_5_norm_norm_weight_to_fp16, x = input_55_cast_fp16)[name = string("op_295_cast_fp16")];
tensor<int32, [3]> input_57_perm_0 = const()[name = string("input_57_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
string h_33_pad_type_0 = const()[name = string("h_33_pad_type_0"), val = string("valid")];
tensor<int32, [1]> h_33_strides_0 = const()[name = string("h_33_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> h_33_pad_0 = const()[name = string("h_33_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> h_33_dilations_0 = const()[name = string("h_33_dilations_0"), val = tensor<int32, [1]>([1])];
int32 h_33_groups_0 = const()[name = string("h_33_groups_0"), val = int32(1)];
tensor<fp16, [256, 64, 1]> m_convnext_5_pwconv1_weight_to_fp16 = const()[name = string("m_convnext_5_pwconv1_weight_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1405376)))];
tensor<fp16, [256]> m_convnext_5_pwconv1_bias_to_fp16 = const()[name = string("m_convnext_5_pwconv1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1438208)))];
tensor<fp16, [1, 64, 129]> input_57_cast_fp16 = transpose(perm = input_57_perm_0, x = var_295_cast_fp16)[name = string("transpose_12")];
tensor<fp16, [1, 256, 129]> h_33_cast_fp16 = conv(bias = m_convnext_5_pwconv1_bias_to_fp16, dilations = h_33_dilations_0, groups = h_33_groups_0, pad = h_33_pad_0, pad_type = h_33_pad_type_0, strides = h_33_strides_0, weight = m_convnext_5_pwconv1_weight_to_fp16, x = input_57_cast_fp16)[name = string("h_33_cast_fp16")];
string input_59_mode_0 = const()[name = string("input_59_mode_0"), val = string("EXACT")];
tensor<fp16, [1, 256, 129]> input_59_cast_fp16 = gelu(mode = input_59_mode_0, x = h_33_cast_fp16)[name = string("input_59_cast_fp16")];
string h_35_pad_type_0 = const()[name = string("h_35_pad_type_0"), val = string("valid")];
tensor<int32, [1]> h_35_strides_0 = const()[name = string("h_35_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> h_35_pad_0 = const()[name = string("h_35_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> h_35_dilations_0 = const()[name = string("h_35_dilations_0"), val = tensor<int32, [1]>([1])];
int32 h_35_groups_0 = const()[name = string("h_35_groups_0"), val = int32(1)];
tensor<fp16, [64, 256, 1]> var_312_weight_0_to_fp16 = const()[name = string("op_312_weight_0_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1438784)))];
tensor<fp16, [64]> var_312_bias_0_to_fp16 = const()[name = string("op_312_bias_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1471616)))];
tensor<fp16, [1, 64, 129]> var_312_cast_fp16 = conv(bias = var_312_bias_0_to_fp16, dilations = h_35_dilations_0, groups = h_35_groups_0, pad = h_35_pad_0, pad_type = h_35_pad_type_0, strides = h_35_strides_0, weight = var_312_weight_0_to_fp16, x = input_59_cast_fp16)[name = string("op_312_cast_fp16")];
tensor<fp16, [1, 64, 129]> out_11_cast_fp16 = add(x = input_51_cast_fp16, y = var_312_cast_fp16)[name = string("out_11_cast_fp16")];
tensor<fp16, [1, 64, 129]> x_29_cast_fp16 = mul(x = out_11_cast_fp16, y = mask_cast_fp16)[name = string("x_29_cast_fp16")];
tensor<fp16, [1, 64, 129]> x_31_cast_fp16 = mul(x = x_29_cast_fp16, y = mask_cast_fp16)[name = string("x_31_cast_fp16")];
string var_335_pad_type_0 = const()[name = string("op_335_pad_type_0"), val = string("valid")];
tensor<int32, [1]> var_335_strides_0 = const()[name = string("op_335_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> var_335_pad_0 = const()[name = string("op_335_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> var_335_dilations_0 = const()[name = string("op_335_dilations_0"), val = tensor<int32, [1]>([1])];
int32 var_335_groups_0 = const()[name = string("op_335_groups_0"), val = int32(1)];
tensor<fp16, [64, 64, 1]> m_attn_layers_0_attn_conv_q_weight_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_q_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1471808)))];
tensor<fp16, [64]> m_attn_layers_0_attn_conv_q_bias_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_q_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1480064)))];
tensor<fp16, [1, 64, 129]> var_335_cast_fp16 = conv(bias = m_attn_layers_0_attn_conv_q_bias_to_fp16, dilations = var_335_dilations_0, groups = var_335_groups_0, pad = var_335_pad_0, pad_type = var_335_pad_type_0, strides = var_335_strides_0, weight = m_attn_layers_0_attn_conv_q_weight_to_fp16, x = x_31_cast_fp16)[name = string("op_335_cast_fp16")];
tensor<int32, [4]> var_336 = const()[name = string("op_336"), val = tensor<int32, [4]>([1, 2, 32, 129])];
tensor<fp16, [1, 2, 32, 129]> var_337_cast_fp16 = reshape(shape = var_336, x = var_335_cast_fp16)[name = string("op_337_cast_fp16")];
tensor<int32, [4]> q_1_perm_0 = const()[name = string("q_1_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
string var_345_pad_type_0 = const()[name = string("op_345_pad_type_0"), val = string("valid")];
tensor<int32, [1]> var_345_strides_0 = const()[name = string("op_345_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> var_345_pad_0 = const()[name = string("op_345_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> var_345_dilations_0 = const()[name = string("op_345_dilations_0"), val = tensor<int32, [1]>([1])];
int32 var_345_groups_0 = const()[name = string("op_345_groups_0"), val = int32(1)];
tensor<fp16, [64, 64, 1]> m_attn_layers_0_attn_conv_k_weight_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_k_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1480256)))];
tensor<fp16, [64]> m_attn_layers_0_attn_conv_k_bias_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_k_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1488512)))];
tensor<fp16, [1, 64, 129]> var_345_cast_fp16 = conv(bias = m_attn_layers_0_attn_conv_k_bias_to_fp16, dilations = var_345_dilations_0, groups = var_345_groups_0, pad = var_345_pad_0, pad_type = var_345_pad_type_0, strides = var_345_strides_0, weight = m_attn_layers_0_attn_conv_k_weight_to_fp16, x = x_31_cast_fp16)[name = string("op_345_cast_fp16")];
tensor<int32, [4]> var_346 = const()[name = string("op_346"), val = tensor<int32, [4]>([1, 2, 32, 129])];
tensor<fp16, [1, 2, 32, 129]> var_347_cast_fp16 = reshape(shape = var_346, x = var_345_cast_fp16)[name = string("op_347_cast_fp16")];
string var_355_pad_type_0 = const()[name = string("op_355_pad_type_0"), val = string("valid")];
tensor<int32, [1]> var_355_strides_0 = const()[name = string("op_355_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> var_355_pad_0 = const()[name = string("op_355_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> var_355_dilations_0 = const()[name = string("op_355_dilations_0"), val = tensor<int32, [1]>([1])];
int32 var_355_groups_0 = const()[name = string("op_355_groups_0"), val = int32(1)];
tensor<fp16, [64, 64, 1]> m_attn_layers_0_attn_conv_v_weight_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_v_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1488704)))];
tensor<fp16, [64]> m_attn_layers_0_attn_conv_v_bias_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_v_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1496960)))];
tensor<fp16, [1, 64, 129]> var_355_cast_fp16 = conv(bias = m_attn_layers_0_attn_conv_v_bias_to_fp16, dilations = var_355_dilations_0, groups = var_355_groups_0, pad = var_355_pad_0, pad_type = var_355_pad_type_0, strides = var_355_strides_0, weight = m_attn_layers_0_attn_conv_v_weight_to_fp16, x = x_31_cast_fp16)[name = string("op_355_cast_fp16")];
tensor<int32, [4]> var_356 = const()[name = string("op_356"), val = tensor<int32, [4]>([1, 2, 32, 129])];
tensor<fp16, [1, 2, 32, 129]> var_357_cast_fp16 = reshape(shape = var_356, x = var_355_cast_fp16)[name = string("op_357_cast_fp16")];
fp16 var_359_to_fp16 = const()[name = string("op_359_to_fp16"), val = fp16(0x1.6ap-3)];
tensor<fp16, [1, 2, 129, 32]> q_1_cast_fp16 = transpose(perm = q_1_perm_0, x = var_337_cast_fp16)[name = string("transpose_11")];
tensor<fp16, [1, 2, 129, 32]> var_360_cast_fp16 = mul(x = q_1_cast_fp16, y = var_359_to_fp16)[name = string("op_360_cast_fp16")];
bool scores_1_transpose_x_0 = const()[name = string("scores_1_transpose_x_0"), val = bool(false)];
bool scores_1_transpose_y_0 = const()[name = string("scores_1_transpose_y_0"), val = bool(false)];
tensor<fp16, [1, 2, 129, 129]> scores_1_cast_fp16 = matmul(transpose_x = scores_1_transpose_x_0, transpose_y = scores_1_transpose_y_0, x = var_360_cast_fp16, y = var_347_cast_fp16)[name = string("scores_1_cast_fp16")];
bool x_33_transpose_x_0 = const()[name = string("x_33_transpose_x_0"), val = bool(false)];
bool x_33_transpose_y_0 = const()[name = string("x_33_transpose_y_0"), val = bool(false)];
tensor<fp16, [1, 1, 32, 257]> var_384_to_fp16 = const()[name = string("op_384_to_fp16"), val = tensor<fp16, [1, 1, 32, 257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1497152)))];
tensor<fp16, [1, 2, 129, 257]> x_33_cast_fp16 = matmul(transpose_x = x_33_transpose_x_0, transpose_y = x_33_transpose_y_0, x = var_360_cast_fp16, y = var_384_to_fp16)[name = string("x_33_cast_fp16")];
tensor<int32, [8]> x_35_pad_0 = const()[name = string("x_35_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 0, 1])];
string x_35_mode_0 = const()[name = string("x_35_mode_0"), val = string("constant")];
fp16 const_13_to_fp16 = const()[name = string("const_13_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 2, 129, 258]> x_35_cast_fp16 = pad(constant_val = const_13_to_fp16, mode = x_35_mode_0, pad = x_35_pad_0, x = x_33_cast_fp16)[name = string("x_35_cast_fp16")];
tensor<int32, [3]> var_396 = const()[name = string("op_396"), val = tensor<int32, [3]>([1, 2, 33282])];
tensor<fp16, [1, 2, 33282]> input_63_cast_fp16 = reshape(shape = var_396, x = x_35_cast_fp16)[name = string("input_63_cast_fp16")];
tensor<int32, [6]> x_37_pad_0 = const()[name = string("x_37_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 0, 128])];
string x_37_mode_0 = const()[name = string("x_37_mode_0"), val = string("constant")];
fp16 const_14_to_fp16 = const()[name = string("const_14_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 2, 33410]> x_37_cast_fp16 = pad(constant_val = const_14_to_fp16, mode = x_37_mode_0, pad = x_37_pad_0, x = input_63_cast_fp16)[name = string("x_37_cast_fp16")];
tensor<int32, [4]> var_411 = const()[name = string("op_411"), val = tensor<int32, [4]>([1, 2, 130, 257])];
tensor<fp16, [1, 2, 130, 257]> x_39_cast_fp16 = reshape(shape = var_411, x = x_37_cast_fp16)[name = string("x_39_cast_fp16")];
tensor<int32, [4]> var_418_begin_0 = const()[name = string("op_418_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_418_end_0 = const()[name = string("op_418_end_0"), val = tensor<int32, [4]>([1, 2, 129, 257])];
tensor<bool, [4]> var_418_end_mask_0 = const()[name = string("op_418_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<fp16, [1, 2, 129, 257]> var_418_cast_fp16 = slice_by_index(begin = var_418_begin_0, end = var_418_end_0, end_mask = var_418_end_mask_0, x = x_39_cast_fp16)[name = string("op_418_cast_fp16")];
tensor<int32, [4]> var_419_begin_0 = const()[name = string("op_419_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 128])];
tensor<int32, [4]> var_419_end_0 = const()[name = string("op_419_end_0"), val = tensor<int32, [4]>([1, 2, 129, 257])];
tensor<bool, [4]> var_419_end_mask_0 = const()[name = string("op_419_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 2, 129, 129]> var_419_cast_fp16 = slice_by_index(begin = var_419_begin_0, end = var_419_end_0, end_mask = var_419_end_mask_0, x = var_418_cast_fp16)[name = string("op_419_cast_fp16")];
tensor<fp16, [1, 2, 129, 129]> scores_3_cast_fp16 = add(x = scores_1_cast_fp16, y = var_419_cast_fp16)[name = string("scores_3_cast_fp16")];
tensor<int32, [1]> var_421_axes_0 = const()[name = string("op_421_axes_0"), val = tensor<int32, [1]>([2])];
tensor<fp16, [1, 1, 1, 129]> var_421_cast_fp16 = expand_dims(axes = var_421_axes_0, x = mask_cast_fp16)[name = string("op_421_cast_fp16")];
fp16 var_10_to_fp16 = const()[name = string("op_10_to_fp16"), val = fp16(0x1p+0)];
tensor<fp16, [1, 1, 1, 129]> var_422_cast_fp16 = sub(x = var_10_to_fp16, y = var_421_cast_fp16)[name = string("op_422_cast_fp16")];
fp16 var_423_to_fp16 = const()[name = string("op_423_to_fp16"), val = fp16(0x1.388p+13)];
tensor<fp16, [1, 1, 1, 129]> var_424_cast_fp16 = mul(x = var_422_cast_fp16, y = var_423_to_fp16)[name = string("op_424_cast_fp16")];
tensor<fp16, [1, 2, 129, 129]> input_65_cast_fp16 = sub(x = scores_3_cast_fp16, y = var_424_cast_fp16)[name = string("input_65_cast_fp16")];
tensor<fp16, [1, 2, 129, 129]> p_attn_1_cast_fp16 = softmax(axis = var_27, x = input_65_cast_fp16)[name = string("p_attn_1_cast_fp16")];
bool out_13_transpose_x_1 = const()[name = string("out_13_transpose_x_1"), val = bool(false)];
bool out_13_transpose_y_1 = const()[name = string("out_13_transpose_y_1"), val = bool(true)];
tensor<fp16, [1, 2, 129, 32]> out_13_cast_fp16 = matmul(transpose_x = out_13_transpose_x_1, transpose_y = out_13_transpose_y_1, x = p_attn_1_cast_fp16, y = var_357_cast_fp16)[name = string("out_13_cast_fp16")];
tensor<int32, [8]> x_41_pad_0 = const()[name = string("x_41_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 0, 128])];
string x_41_mode_0 = const()[name = string("x_41_mode_0"), val = string("constant")];
fp16 const_19_to_fp16 = const()[name = string("const_19_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 2, 129, 257]> x_41_cast_fp16 = pad(constant_val = const_19_to_fp16, mode = x_41_mode_0, pad = x_41_pad_0, x = p_attn_1_cast_fp16)[name = string("x_41_cast_fp16")];
tensor<int32, [3]> var_461 = const()[name = string("op_461"), val = tensor<int32, [3]>([1, 2, 33153])];
tensor<fp16, [1, 2, 33153]> input_69_cast_fp16 = reshape(shape = var_461, x = x_41_cast_fp16)[name = string("input_69_cast_fp16")];
tensor<int32, [6]> x_43_pad_0 = const()[name = string("x_43_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 129, 0])];
string x_43_mode_0 = const()[name = string("x_43_mode_0"), val = string("constant")];
fp16 const_20_to_fp16 = const()[name = string("const_20_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 2, 33282]> x_43_cast_fp16 = pad(constant_val = const_20_to_fp16, mode = x_43_mode_0, pad = x_43_pad_0, x = input_69_cast_fp16)[name = string("x_43_cast_fp16")];
tensor<int32, [4]> var_468 = const()[name = string("op_468"), val = tensor<int32, [4]>([1, 2, 129, 258])];
tensor<fp16, [1, 2, 129, 258]> x_45_cast_fp16 = reshape(shape = var_468, x = x_43_cast_fp16)[name = string("x_45_cast_fp16")];
tensor<int32, [4]> rel_weights_1_begin_0 = const()[name = string("rel_weights_1_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 1])];
tensor<int32, [4]> rel_weights_1_end_0 = const()[name = string("rel_weights_1_end_0"), val = tensor<int32, [4]>([1, 2, 129, 258])];
tensor<bool, [4]> rel_weights_1_end_mask_0 = const()[name = string("rel_weights_1_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 2, 129, 257]> rel_weights_1_cast_fp16 = slice_by_index(begin = rel_weights_1_begin_0, end = rel_weights_1_end_0, end_mask = rel_weights_1_end_mask_0, x = x_45_cast_fp16)[name = string("rel_weights_1_cast_fp16")];
bool var_475_transpose_x_0 = const()[name = string("op_475_transpose_x_0"), val = bool(false)];
bool var_475_transpose_y_0 = const()[name = string("op_475_transpose_y_0"), val = bool(false)];
tensor<fp16, [1, 1, 257, 32]> var_474_to_fp16 = const()[name = string("op_474_to_fp16"), val = tensor<fp16, [1, 1, 257, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1513664)))];
tensor<fp16, [1, 2, 129, 32]> var_475_cast_fp16 = matmul(transpose_x = var_475_transpose_x_0, transpose_y = var_475_transpose_y_0, x = rel_weights_1_cast_fp16, y = var_474_to_fp16)[name = string("op_475_cast_fp16")];
tensor<fp16, [1, 2, 129, 32]> out_15_cast_fp16 = add(x = out_13_cast_fp16, y = var_475_cast_fp16)[name = string("out_15_cast_fp16")];
tensor<int32, [4]> var_477_perm_0 = const()[name = string("op_477_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
tensor<int32, [3]> var_478 = const()[name = string("op_478"), val = tensor<int32, [3]>([1, 64, 129])];
tensor<fp16, [1, 2, 32, 129]> var_477_cast_fp16 = transpose(perm = var_477_perm_0, x = out_15_cast_fp16)[name = string("transpose_10")];
tensor<fp16, [1, 64, 129]> input_71_cast_fp16 = reshape(shape = var_478, x = var_477_cast_fp16)[name = string("input_71_cast_fp16")];
string y_1_pad_type_0 = const()[name = string("y_1_pad_type_0"), val = string("valid")];
tensor<int32, [1]> y_1_strides_0 = const()[name = string("y_1_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> y_1_pad_0 = const()[name = string("y_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> y_1_dilations_0 = const()[name = string("y_1_dilations_0"), val = tensor<int32, [1]>([1])];
int32 y_1_groups_0 = const()[name = string("y_1_groups_0"), val = int32(1)];
tensor<fp16, [64, 64, 1]> m_attn_layers_0_attn_conv_o_weight_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_o_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530176)))];
tensor<fp16, [64]> m_attn_layers_0_attn_conv_o_bias_to_fp16 = const()[name = string("m_attn_layers_0_attn_conv_o_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1538432)))];
tensor<fp16, [1, 64, 129]> y_1_cast_fp16 = conv(bias = m_attn_layers_0_attn_conv_o_bias_to_fp16, dilations = y_1_dilations_0, groups = y_1_groups_0, pad = y_1_pad_0, pad_type = y_1_pad_type_0, strides = y_1_strides_0, weight = m_attn_layers_0_attn_conv_o_weight_to_fp16, x = input_71_cast_fp16)[name = string("y_1_cast_fp16")];
tensor<fp16, [1, 64, 129]> x_47_cast_fp16 = add(x = x_31_cast_fp16, y = y_1_cast_fp16)[name = string("x_47_cast_fp16")];
tensor<int32, [3]> input_73_perm_0 = const()[name = string("input_73_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<int32, [1]> var_493_axes_0 = const()[name = string("op_493_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [64]> m_attn_layers_0_norm_1_norm_weight_to_fp16 = const()[name = string("m_attn_layers_0_norm_1_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1538624)))];
tensor<fp16, [64]> m_attn_layers_0_norm_1_norm_bias_to_fp16 = const()[name = string("m_attn_layers_0_norm_1_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1538816)))];
tensor<fp16, [1, 129, 64]> input_73_cast_fp16 = transpose(perm = input_73_perm_0, x = x_47_cast_fp16)[name = string("transpose_9")];
tensor<fp16, [1, 129, 64]> var_493_cast_fp16 = layer_norm(axes = var_493_axes_0, beta = m_attn_layers_0_norm_1_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_attn_layers_0_norm_1_norm_weight_to_fp16, x = input_73_cast_fp16)[name = string("op_493_cast_fp16")];
tensor<int32, [3]> x_49_perm_0 = const()[name = string("x_49_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<fp16, [1, 64, 129]> x_49_cast_fp16 = transpose(perm = x_49_perm_0, x = var_493_cast_fp16)[name = string("transpose_8")];
tensor<fp16, [1, 64, 129]> input_75_cast_fp16 = mul(x = x_49_cast_fp16, y = mask_cast_fp16)[name = string("input_75_cast_fp16")];
string input_77_pad_type_0 = const()[name = string("input_77_pad_type_0"), val = string("valid")];
tensor<int32, [1]> input_77_strides_0 = const()[name = string("input_77_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> input_77_pad_0 = const()[name = string("input_77_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> input_77_dilations_0 = const()[name = string("input_77_dilations_0"), val = tensor<int32, [1]>([1])];
int32 input_77_groups_0 = const()[name = string("input_77_groups_0"), val = int32(1)];
tensor<fp16, [256, 64, 1]> m_attn_layers_0_ffn_conv_1_weight_to_fp16 = const()[name = string("m_attn_layers_0_ffn_conv_1_weight_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1539008)))];
tensor<fp16, [256]> m_attn_layers_0_ffn_conv_1_bias_to_fp16 = const()[name = string("m_attn_layers_0_ffn_conv_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1571840)))];
tensor<fp16, [1, 256, 129]> input_77_cast_fp16 = conv(bias = m_attn_layers_0_ffn_conv_1_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 = m_attn_layers_0_ffn_conv_1_weight_to_fp16, x = input_75_cast_fp16)[name = string("input_77_cast_fp16")];
tensor<fp16, [1, 256, 129]> h_37_cast_fp16 = relu(x = input_77_cast_fp16)[name = string("h_37_cast_fp16")];
tensor<fp16, [1, 256, 129]> input_79_cast_fp16 = mul(x = h_37_cast_fp16, y = mask_cast_fp16)[name = string("input_79_cast_fp16")];
string h_39_pad_type_0 = const()[name = string("h_39_pad_type_0"), val = string("valid")];
tensor<int32, [1]> h_39_strides_0 = const()[name = string("h_39_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> h_39_pad_0 = const()[name = string("h_39_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> h_39_dilations_0 = const()[name = string("h_39_dilations_0"), val = tensor<int32, [1]>([1])];
int32 h_39_groups_0 = const()[name = string("h_39_groups_0"), val = int32(1)];
tensor<fp16, [64, 256, 1]> m_attn_layers_0_ffn_conv_2_weight_to_fp16 = const()[name = string("m_attn_layers_0_ffn_conv_2_weight_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1572416)))];
tensor<fp16, [64]> m_attn_layers_0_ffn_conv_2_bias_to_fp16 = const()[name = string("m_attn_layers_0_ffn_conv_2_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1605248)))];
tensor<fp16, [1, 64, 129]> h_39_cast_fp16 = conv(bias = m_attn_layers_0_ffn_conv_2_bias_to_fp16, dilations = h_39_dilations_0, groups = h_39_groups_0, pad = h_39_pad_0, pad_type = h_39_pad_type_0, strides = h_39_strides_0, weight = m_attn_layers_0_ffn_conv_2_weight_to_fp16, x = input_79_cast_fp16)[name = string("h_39_cast_fp16")];
tensor<fp16, [1, 64, 129]> y_3_cast_fp16 = mul(x = h_39_cast_fp16, y = mask_cast_fp16)[name = string("y_3_cast_fp16")];
tensor<fp16, [1, 64, 129]> x_51_cast_fp16 = add(x = x_49_cast_fp16, y = y_3_cast_fp16)[name = string("x_51_cast_fp16")];
tensor<int32, [3]> input_81_perm_0 = const()[name = string("input_81_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<int32, [1]> var_521_axes_0 = const()[name = string("op_521_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [64]> m_attn_layers_0_norm_2_norm_weight_to_fp16 = const()[name = string("m_attn_layers_0_norm_2_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1605440)))];
tensor<fp16, [64]> m_attn_layers_0_norm_2_norm_bias_to_fp16 = const()[name = string("m_attn_layers_0_norm_2_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1605632)))];
tensor<fp16, [1, 129, 64]> input_81_cast_fp16 = transpose(perm = input_81_perm_0, x = x_51_cast_fp16)[name = string("transpose_7")];
tensor<fp16, [1, 129, 64]> var_521_cast_fp16 = layer_norm(axes = var_521_axes_0, beta = m_attn_layers_0_norm_2_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_attn_layers_0_norm_2_norm_weight_to_fp16, x = input_81_cast_fp16)[name = string("op_521_cast_fp16")];
tensor<int32, [3]> x_53_perm_0 = const()[name = string("x_53_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<fp16, [1, 64, 129]> x_53_cast_fp16 = transpose(perm = x_53_perm_0, x = var_521_cast_fp16)[name = string("transpose_6")];
tensor<fp16, [1, 64, 129]> x_55_cast_fp16 = mul(x = x_53_cast_fp16, y = mask_cast_fp16)[name = string("x_55_cast_fp16")];
string var_543_pad_type_0 = const()[name = string("op_543_pad_type_0"), val = string("valid")];
tensor<int32, [1]> var_543_strides_0 = const()[name = string("op_543_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> var_543_pad_0 = const()[name = string("op_543_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> var_543_dilations_0 = const()[name = string("op_543_dilations_0"), val = tensor<int32, [1]>([1])];
int32 var_543_groups_0 = const()[name = string("op_543_groups_0"), val = int32(1)];
tensor<fp16, [64, 64, 1]> m_attn_layers_1_attn_conv_q_weight_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_q_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1605824)))];
tensor<fp16, [64]> m_attn_layers_1_attn_conv_q_bias_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_q_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1614080)))];
tensor<fp16, [1, 64, 129]> var_543_cast_fp16 = conv(bias = m_attn_layers_1_attn_conv_q_bias_to_fp16, dilations = var_543_dilations_0, groups = var_543_groups_0, pad = var_543_pad_0, pad_type = var_543_pad_type_0, strides = var_543_strides_0, weight = m_attn_layers_1_attn_conv_q_weight_to_fp16, x = x_55_cast_fp16)[name = string("op_543_cast_fp16")];
tensor<int32, [4]> var_544 = const()[name = string("op_544"), val = tensor<int32, [4]>([1, 2, 32, 129])];
tensor<fp16, [1, 2, 32, 129]> var_545_cast_fp16 = reshape(shape = var_544, x = var_543_cast_fp16)[name = string("op_545_cast_fp16")];
tensor<int32, [4]> q_perm_0 = const()[name = string("q_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
string var_553_pad_type_0 = const()[name = string("op_553_pad_type_0"), val = string("valid")];
tensor<int32, [1]> var_553_strides_0 = const()[name = string("op_553_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> var_553_pad_0 = const()[name = string("op_553_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> var_553_dilations_0 = const()[name = string("op_553_dilations_0"), val = tensor<int32, [1]>([1])];
int32 var_553_groups_0 = const()[name = string("op_553_groups_0"), val = int32(1)];
tensor<fp16, [64, 64, 1]> m_attn_layers_1_attn_conv_k_weight_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_k_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1614272)))];
tensor<fp16, [64]> m_attn_layers_1_attn_conv_k_bias_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_k_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1622528)))];
tensor<fp16, [1, 64, 129]> var_553_cast_fp16 = conv(bias = m_attn_layers_1_attn_conv_k_bias_to_fp16, dilations = var_553_dilations_0, groups = var_553_groups_0, pad = var_553_pad_0, pad_type = var_553_pad_type_0, strides = var_553_strides_0, weight = m_attn_layers_1_attn_conv_k_weight_to_fp16, x = x_55_cast_fp16)[name = string("op_553_cast_fp16")];
tensor<int32, [4]> var_554 = const()[name = string("op_554"), val = tensor<int32, [4]>([1, 2, 32, 129])];
tensor<fp16, [1, 2, 32, 129]> var_555_cast_fp16 = reshape(shape = var_554, x = var_553_cast_fp16)[name = string("op_555_cast_fp16")];
string var_563_pad_type_0 = const()[name = string("op_563_pad_type_0"), val = string("valid")];
tensor<int32, [1]> var_563_strides_0 = const()[name = string("op_563_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> var_563_pad_0 = const()[name = string("op_563_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> var_563_dilations_0 = const()[name = string("op_563_dilations_0"), val = tensor<int32, [1]>([1])];
int32 var_563_groups_0 = const()[name = string("op_563_groups_0"), val = int32(1)];
tensor<fp16, [64, 64, 1]> m_attn_layers_1_attn_conv_v_weight_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_v_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1622720)))];
tensor<fp16, [64]> m_attn_layers_1_attn_conv_v_bias_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_v_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1630976)))];
tensor<fp16, [1, 64, 129]> var_563_cast_fp16 = conv(bias = m_attn_layers_1_attn_conv_v_bias_to_fp16, dilations = var_563_dilations_0, groups = var_563_groups_0, pad = var_563_pad_0, pad_type = var_563_pad_type_0, strides = var_563_strides_0, weight = m_attn_layers_1_attn_conv_v_weight_to_fp16, x = x_55_cast_fp16)[name = string("op_563_cast_fp16")];
tensor<int32, [4]> var_564 = const()[name = string("op_564"), val = tensor<int32, [4]>([1, 2, 32, 129])];
tensor<fp16, [1, 2, 32, 129]> var_565_cast_fp16 = reshape(shape = var_564, x = var_563_cast_fp16)[name = string("op_565_cast_fp16")];
fp16 var_567_to_fp16 = const()[name = string("op_567_to_fp16"), val = fp16(0x1.6ap-3)];
tensor<fp16, [1, 2, 129, 32]> q_cast_fp16 = transpose(perm = q_perm_0, x = var_545_cast_fp16)[name = string("transpose_5")];
tensor<fp16, [1, 2, 129, 32]> var_568_cast_fp16 = mul(x = q_cast_fp16, y = var_567_to_fp16)[name = string("op_568_cast_fp16")];
bool scores_5_transpose_x_0 = const()[name = string("scores_5_transpose_x_0"), val = bool(false)];
bool scores_5_transpose_y_0 = const()[name = string("scores_5_transpose_y_0"), val = bool(false)];
tensor<fp16, [1, 2, 129, 129]> scores_5_cast_fp16 = matmul(transpose_x = scores_5_transpose_x_0, transpose_y = scores_5_transpose_y_0, x = var_568_cast_fp16, y = var_555_cast_fp16)[name = string("scores_5_cast_fp16")];
bool x_57_transpose_x_0 = const()[name = string("x_57_transpose_x_0"), val = bool(false)];
bool x_57_transpose_y_0 = const()[name = string("x_57_transpose_y_0"), val = bool(false)];
tensor<fp16, [1, 1, 32, 257]> var_592_to_fp16 = const()[name = string("op_592_to_fp16"), val = tensor<fp16, [1, 1, 32, 257]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1631168)))];
tensor<fp16, [1, 2, 129, 257]> x_57_cast_fp16 = matmul(transpose_x = x_57_transpose_x_0, transpose_y = x_57_transpose_y_0, x = var_568_cast_fp16, y = var_592_to_fp16)[name = string("x_57_cast_fp16")];
tensor<int32, [8]> x_59_pad_0 = const()[name = string("x_59_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 0, 1])];
string x_59_mode_0 = const()[name = string("x_59_mode_0"), val = string("constant")];
fp16 const_27_to_fp16 = const()[name = string("const_27_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 2, 129, 258]> x_59_cast_fp16 = pad(constant_val = const_27_to_fp16, mode = x_59_mode_0, pad = x_59_pad_0, x = x_57_cast_fp16)[name = string("x_59_cast_fp16")];
tensor<int32, [3]> var_604 = const()[name = string("op_604"), val = tensor<int32, [3]>([1, 2, 33282])];
tensor<fp16, [1, 2, 33282]> input_85_cast_fp16 = reshape(shape = var_604, x = x_59_cast_fp16)[name = string("input_85_cast_fp16")];
tensor<int32, [6]> x_61_pad_0 = const()[name = string("x_61_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 0, 128])];
string x_61_mode_0 = const()[name = string("x_61_mode_0"), val = string("constant")];
fp16 const_28_to_fp16 = const()[name = string("const_28_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 2, 33410]> x_61_cast_fp16 = pad(constant_val = const_28_to_fp16, mode = x_61_mode_0, pad = x_61_pad_0, x = input_85_cast_fp16)[name = string("x_61_cast_fp16")];
tensor<int32, [4]> var_619 = const()[name = string("op_619"), val = tensor<int32, [4]>([1, 2, 130, 257])];
tensor<fp16, [1, 2, 130, 257]> x_63_cast_fp16 = reshape(shape = var_619, x = x_61_cast_fp16)[name = string("x_63_cast_fp16")];
tensor<int32, [4]> var_626_begin_0 = const()[name = string("op_626_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
tensor<int32, [4]> var_626_end_0 = const()[name = string("op_626_end_0"), val = tensor<int32, [4]>([1, 2, 129, 257])];
tensor<bool, [4]> var_626_end_mask_0 = const()[name = string("op_626_end_mask_0"), val = tensor<bool, [4]>([true, true, false, true])];
tensor<fp16, [1, 2, 129, 257]> var_626_cast_fp16 = slice_by_index(begin = var_626_begin_0, end = var_626_end_0, end_mask = var_626_end_mask_0, x = x_63_cast_fp16)[name = string("op_626_cast_fp16")];
tensor<int32, [4]> var_627_begin_0 = const()[name = string("op_627_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 128])];
tensor<int32, [4]> var_627_end_0 = const()[name = string("op_627_end_0"), val = tensor<int32, [4]>([1, 2, 129, 257])];
tensor<bool, [4]> var_627_end_mask_0 = const()[name = string("op_627_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 2, 129, 129]> var_627_cast_fp16 = slice_by_index(begin = var_627_begin_0, end = var_627_end_0, end_mask = var_627_end_mask_0, x = var_626_cast_fp16)[name = string("op_627_cast_fp16")];
tensor<fp16, [1, 2, 129, 129]> scores_cast_fp16 = add(x = scores_5_cast_fp16, y = var_627_cast_fp16)[name = string("scores_cast_fp16")];
tensor<fp16, [1, 2, 129, 129]> input_87_cast_fp16 = sub(x = scores_cast_fp16, y = var_424_cast_fp16)[name = string("input_87_cast_fp16")];
tensor<fp16, [1, 2, 129, 129]> p_attn_cast_fp16 = softmax(axis = var_27, x = input_87_cast_fp16)[name = string("p_attn_cast_fp16")];
bool out_17_transpose_x_1 = const()[name = string("out_17_transpose_x_1"), val = bool(false)];
bool out_17_transpose_y_1 = const()[name = string("out_17_transpose_y_1"), val = bool(true)];
tensor<fp16, [1, 2, 129, 32]> out_17_cast_fp16 = matmul(transpose_x = out_17_transpose_x_1, transpose_y = out_17_transpose_y_1, x = p_attn_cast_fp16, y = var_565_cast_fp16)[name = string("out_17_cast_fp16")];
tensor<int32, [8]> x_65_pad_0 = const()[name = string("x_65_pad_0"), val = tensor<int32, [8]>([0, 0, 0, 0, 0, 0, 0, 128])];
string x_65_mode_0 = const()[name = string("x_65_mode_0"), val = string("constant")];
fp16 const_33_to_fp16 = const()[name = string("const_33_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 2, 129, 257]> x_65_cast_fp16 = pad(constant_val = const_33_to_fp16, mode = x_65_mode_0, pad = x_65_pad_0, x = p_attn_cast_fp16)[name = string("x_65_cast_fp16")];
tensor<int32, [3]> var_669 = const()[name = string("op_669"), val = tensor<int32, [3]>([1, 2, 33153])];
tensor<fp16, [1, 2, 33153]> input_91_cast_fp16 = reshape(shape = var_669, x = x_65_cast_fp16)[name = string("input_91_cast_fp16")];
tensor<int32, [6]> x_67_pad_0 = const()[name = string("x_67_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 129, 0])];
string x_67_mode_0 = const()[name = string("x_67_mode_0"), val = string("constant")];
fp16 const_34_to_fp16 = const()[name = string("const_34_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 2, 33282]> x_67_cast_fp16 = pad(constant_val = const_34_to_fp16, mode = x_67_mode_0, pad = x_67_pad_0, x = input_91_cast_fp16)[name = string("x_67_cast_fp16")];
tensor<int32, [4]> var_676 = const()[name = string("op_676"), val = tensor<int32, [4]>([1, 2, 129, 258])];
tensor<fp16, [1, 2, 129, 258]> x_69_cast_fp16 = reshape(shape = var_676, x = x_67_cast_fp16)[name = string("x_69_cast_fp16")];
tensor<int32, [4]> rel_weights_begin_0 = const()[name = string("rel_weights_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 1])];
tensor<int32, [4]> rel_weights_end_0 = const()[name = string("rel_weights_end_0"), val = tensor<int32, [4]>([1, 2, 129, 258])];
tensor<bool, [4]> rel_weights_end_mask_0 = const()[name = string("rel_weights_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
tensor<fp16, [1, 2, 129, 257]> rel_weights_cast_fp16 = slice_by_index(begin = rel_weights_begin_0, end = rel_weights_end_0, end_mask = rel_weights_end_mask_0, x = x_69_cast_fp16)[name = string("rel_weights_cast_fp16")];
bool var_683_transpose_x_0 = const()[name = string("op_683_transpose_x_0"), val = bool(false)];
bool var_683_transpose_y_0 = const()[name = string("op_683_transpose_y_0"), val = bool(false)];
tensor<fp16, [1, 1, 257, 32]> var_682_to_fp16 = const()[name = string("op_682_to_fp16"), val = tensor<fp16, [1, 1, 257, 32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1647680)))];
tensor<fp16, [1, 2, 129, 32]> var_683_cast_fp16 = matmul(transpose_x = var_683_transpose_x_0, transpose_y = var_683_transpose_y_0, x = rel_weights_cast_fp16, y = var_682_to_fp16)[name = string("op_683_cast_fp16")];
tensor<fp16, [1, 2, 129, 32]> out_cast_fp16 = add(x = out_17_cast_fp16, y = var_683_cast_fp16)[name = string("out_cast_fp16")];
tensor<int32, [4]> var_685_perm_0 = const()[name = string("op_685_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
tensor<int32, [3]> var_686 = const()[name = string("op_686"), val = tensor<int32, [3]>([1, 64, 129])];
tensor<fp16, [1, 2, 32, 129]> var_685_cast_fp16 = transpose(perm = var_685_perm_0, x = out_cast_fp16)[name = string("transpose_4")];
tensor<fp16, [1, 64, 129]> input_93_cast_fp16 = reshape(shape = var_686, x = var_685_cast_fp16)[name = string("input_93_cast_fp16")];
string y_5_pad_type_0 = const()[name = string("y_5_pad_type_0"), val = string("valid")];
tensor<int32, [1]> y_5_strides_0 = const()[name = string("y_5_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> y_5_pad_0 = const()[name = string("y_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> y_5_dilations_0 = const()[name = string("y_5_dilations_0"), val = tensor<int32, [1]>([1])];
int32 y_5_groups_0 = const()[name = string("y_5_groups_0"), val = int32(1)];
tensor<fp16, [64, 64, 1]> m_attn_layers_1_attn_conv_o_weight_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_o_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1664192)))];
tensor<fp16, [64]> m_attn_layers_1_attn_conv_o_bias_to_fp16 = const()[name = string("m_attn_layers_1_attn_conv_o_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672448)))];
tensor<fp16, [1, 64, 129]> y_5_cast_fp16 = conv(bias = m_attn_layers_1_attn_conv_o_bias_to_fp16, dilations = y_5_dilations_0, groups = y_5_groups_0, pad = y_5_pad_0, pad_type = y_5_pad_type_0, strides = y_5_strides_0, weight = m_attn_layers_1_attn_conv_o_weight_to_fp16, x = input_93_cast_fp16)[name = string("y_5_cast_fp16")];
tensor<fp16, [1, 64, 129]> x_71_cast_fp16 = add(x = x_55_cast_fp16, y = y_5_cast_fp16)[name = string("x_71_cast_fp16")];
tensor<int32, [3]> input_95_perm_0 = const()[name = string("input_95_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<int32, [1]> var_701_axes_0 = const()[name = string("op_701_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [64]> m_attn_layers_1_norm_1_norm_weight_to_fp16 = const()[name = string("m_attn_layers_1_norm_1_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672640)))];
tensor<fp16, [64]> m_attn_layers_1_norm_1_norm_bias_to_fp16 = const()[name = string("m_attn_layers_1_norm_1_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1672832)))];
tensor<fp16, [1, 129, 64]> input_95_cast_fp16 = transpose(perm = input_95_perm_0, x = x_71_cast_fp16)[name = string("transpose_3")];
tensor<fp16, [1, 129, 64]> var_701_cast_fp16 = layer_norm(axes = var_701_axes_0, beta = m_attn_layers_1_norm_1_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_attn_layers_1_norm_1_norm_weight_to_fp16, x = input_95_cast_fp16)[name = string("op_701_cast_fp16")];
tensor<int32, [3]> x_73_perm_0 = const()[name = string("x_73_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<fp16, [1, 64, 129]> x_73_cast_fp16 = transpose(perm = x_73_perm_0, x = var_701_cast_fp16)[name = string("transpose_2")];
tensor<fp16, [1, 64, 129]> input_97_cast_fp16 = mul(x = x_73_cast_fp16, y = mask_cast_fp16)[name = string("input_97_cast_fp16")];
string input_99_pad_type_0 = const()[name = string("input_99_pad_type_0"), val = string("valid")];
tensor<int32, [1]> input_99_strides_0 = const()[name = string("input_99_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> input_99_pad_0 = const()[name = string("input_99_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> input_99_dilations_0 = const()[name = string("input_99_dilations_0"), val = tensor<int32, [1]>([1])];
int32 input_99_groups_0 = const()[name = string("input_99_groups_0"), val = int32(1)];
tensor<fp16, [256, 64, 1]> m_attn_layers_1_ffn_conv_1_weight_to_fp16 = const()[name = string("m_attn_layers_1_ffn_conv_1_weight_to_fp16"), val = tensor<fp16, [256, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1673024)))];
tensor<fp16, [256]> m_attn_layers_1_ffn_conv_1_bias_to_fp16 = const()[name = string("m_attn_layers_1_ffn_conv_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1705856)))];
tensor<fp16, [1, 256, 129]> input_99_cast_fp16 = conv(bias = m_attn_layers_1_ffn_conv_1_bias_to_fp16, dilations = input_99_dilations_0, groups = input_99_groups_0, pad = input_99_pad_0, pad_type = input_99_pad_type_0, strides = input_99_strides_0, weight = m_attn_layers_1_ffn_conv_1_weight_to_fp16, x = input_97_cast_fp16)[name = string("input_99_cast_fp16")];
tensor<fp16, [1, 256, 129]> h_41_cast_fp16 = relu(x = input_99_cast_fp16)[name = string("h_41_cast_fp16")];
tensor<fp16, [1, 256, 129]> input_101_cast_fp16 = mul(x = h_41_cast_fp16, y = mask_cast_fp16)[name = string("input_101_cast_fp16")];
string h_pad_type_0 = const()[name = string("h_pad_type_0"), val = string("valid")];
tensor<int32, [1]> h_strides_0 = const()[name = string("h_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> h_pad_0 = const()[name = string("h_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> h_dilations_0 = const()[name = string("h_dilations_0"), val = tensor<int32, [1]>([1])];
int32 h_groups_0 = const()[name = string("h_groups_0"), val = int32(1)];
tensor<fp16, [64, 256, 1]> m_attn_layers_1_ffn_conv_2_weight_to_fp16 = const()[name = string("m_attn_layers_1_ffn_conv_2_weight_to_fp16"), val = tensor<fp16, [64, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1706432)))];
tensor<fp16, [64]> m_attn_layers_1_ffn_conv_2_bias_to_fp16 = const()[name = string("m_attn_layers_1_ffn_conv_2_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1739264)))];
tensor<fp16, [1, 64, 129]> h_cast_fp16 = conv(bias = m_attn_layers_1_ffn_conv_2_bias_to_fp16, dilations = h_dilations_0, groups = h_groups_0, pad = h_pad_0, pad_type = h_pad_type_0, strides = h_strides_0, weight = m_attn_layers_1_ffn_conv_2_weight_to_fp16, x = input_101_cast_fp16)[name = string("h_cast_fp16")];
tensor<fp16, [1, 64, 129]> y_cast_fp16 = mul(x = h_cast_fp16, y = mask_cast_fp16)[name = string("y_cast_fp16")];
tensor<fp16, [1, 64, 129]> x_75_cast_fp16 = add(x = x_73_cast_fp16, y = y_cast_fp16)[name = string("x_75_cast_fp16")];
tensor<int32, [3]> input_103_perm_0 = const()[name = string("input_103_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<int32, [1]> var_729_axes_0 = const()[name = string("op_729_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [64]> m_attn_layers_1_norm_2_norm_weight_to_fp16 = const()[name = string("m_attn_layers_1_norm_2_norm_weight_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1739456)))];
tensor<fp16, [64]> m_attn_layers_1_norm_2_norm_bias_to_fp16 = const()[name = string("m_attn_layers_1_norm_2_norm_bias_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1739648)))];
tensor<fp16, [1, 129, 64]> input_103_cast_fp16 = transpose(perm = input_103_perm_0, x = x_75_cast_fp16)[name = string("transpose_1")];
tensor<fp16, [1, 129, 64]> var_729_cast_fp16 = layer_norm(axes = var_729_axes_0, beta = m_attn_layers_1_norm_2_norm_bias_to_fp16, epsilon = var_20_to_fp16, gamma = m_attn_layers_1_norm_2_norm_weight_to_fp16, x = input_103_cast_fp16)[name = string("op_729_cast_fp16")];
tensor<int32, [3]> x_77_perm_0 = const()[name = string("x_77_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<fp16, [1, 64, 129]> x_77_cast_fp16 = transpose(perm = x_77_perm_0, x = var_729_cast_fp16)[name = string("transpose_0")];
tensor<fp16, [1, 64, 129]> x_79_cast_fp16 = mul(x = x_77_cast_fp16, y = mask_cast_fp16)[name = string("x_79_cast_fp16")];
tensor<fp16, [1, 64, 129]> x_81_cast_fp16 = add(x = x_79_cast_fp16, y = x_29_cast_fp16)[name = string("x_81_cast_fp16")];
tensor<int32, [3]> input_105_begin_0 = const()[name = string("input_105_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
tensor<int32, [3]> input_105_end_0 = const()[name = string("input_105_end_0"), val = tensor<int32, [3]>([1, 64, 1])];
tensor<bool, [3]> input_105_end_mask_0 = const()[name = string("input_105_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
tensor<fp16, [1, 64, 1]> input_105_cast_fp16 = slice_by_index(begin = input_105_begin_0, end = input_105_end_0, end_mask = input_105_end_mask_0, x = x_81_cast_fp16)[name = string("input_105_cast_fp16")];
tensor<int32, [3]> sentence_mask_begin_0 = const()[name = string("sentence_mask_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
tensor<int32, [3]> sentence_mask_end_0 = const()[name = string("sentence_mask_end_0"), val = tensor<int32, [3]>([1, 1, 1])];
tensor<bool, [3]> sentence_mask_end_mask_0 = const()[name = string("sentence_mask_end_mask_0"), val = tensor<bool, [3]>([true, true, false])];
tensor<fp16, [1, 1, 1]> sentence_mask_cast_fp16 = slice_by_index(begin = sentence_mask_begin_0, end = sentence_mask_end_0, end_mask = sentence_mask_end_mask_0, x = mask_cast_fp16)[name = string("sentence_mask_cast_fp16")];
string var_744_pad_type_0 = const()[name = string("op_744_pad_type_0"), val = string("valid")];
tensor<int32, [1]> var_744_strides_0 = const()[name = string("op_744_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> var_744_pad_0 = const()[name = string("op_744_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> var_744_dilations_0 = const()[name = string("op_744_dilations_0"), val = tensor<int32, [1]>([1])];
int32 var_744_groups_0 = const()[name = string("op_744_groups_0"), val = int32(1)];
tensor<fp16, [64, 64, 1]> m_proj_out_conv_weight_to_fp16 = const()[name = string("m_proj_out_conv_weight_to_fp16"), val = tensor<fp16, [64, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1739840)))];
tensor<fp16, [1, 64, 1]> var_744_cast_fp16 = conv(dilations = var_744_dilations_0, groups = var_744_groups_0, pad = var_744_pad_0, pad_type = var_744_pad_type_0, strides = var_744_strides_0, weight = m_proj_out_conv_weight_to_fp16, x = input_105_cast_fp16)[name = string("op_744_cast_fp16")];
tensor<fp16, [1, 64, 1]> sentence_emb_cast_fp16 = mul(x = var_744_cast_fp16, y = sentence_mask_cast_fp16)[name = string("sentence_emb_cast_fp16")];
tensor<int32, [2]> var_752 = const()[name = string("op_752"), val = tensor<int32, [2]>([1, -1])];
tensor<fp16, [1, 64]> s_cast_fp16 = reshape(shape = var_752, x = sentence_emb_cast_fp16)[name = string("s_cast_fp16")];
tensor<int32, [2]> var_754 = const()[name = string("op_754"), val = tensor<int32, [2]>([1, -1])];
string style_dp_to_fp16_dtype_0 = const()[name = string("style_dp_to_fp16_dtype_0"), val = string("fp16")];
tensor<fp16, [1, 8, 16]> style_dp_to_fp16 = cast(dtype = style_dp_to_fp16_dtype_0, x = style_dp)[name = string("cast_21")];
tensor<fp16, [1, 128]> v_cast_fp16 = reshape(shape = var_754, x = style_dp_to_fp16)[name = string("v_cast_fp16")];
bool input_107_interleave_0 = const()[name = string("input_107_interleave_0"), val = bool(false)];
tensor<fp16, [1, 192]> input_107_cast_fp16 = concat(axis = var_26, interleave = input_107_interleave_0, values = (s_cast_fp16, v_cast_fp16))[name = string("input_107_cast_fp16")];
tensor<fp16, [128, 192]> m_predictor_layers_0_weight_to_fp16 = const()[name = string("m_predictor_layers_0_weight_to_fp16"), val = tensor<fp16, [128, 192]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1748096)))];
tensor<fp16, [128]> m_predictor_layers_0_bias_to_fp16 = const()[name = string("m_predictor_layers_0_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1797312)))];
tensor<fp16, [1, 128]> linear_0_cast_fp16 = linear(bias = m_predictor_layers_0_bias_to_fp16, weight = m_predictor_layers_0_weight_to_fp16, x = input_107_cast_fp16)[name = string("linear_0_cast_fp16")];
tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor<int32, [1]>([2])];
tensor<fp16, [1, 128, 1]> expand_dims_0_cast_fp16 = expand_dims(axes = expand_dims_0_axes_0, x = linear_0_cast_fp16)[name = string("expand_dims_0_cast_fp16")];
fp32 prelu_0_alpha_1 = const()[name = string("prelu_0_alpha_1"), val = fp32(0x1.b19f5ap-3)];
tensor<fp16, [1, 128, 1]> prelu_0_cast_fp16 = leaky_relu(alpha = prelu_0_alpha_1, x = expand_dims_0_cast_fp16)[name = string("prelu_0_cast_fp16")];
tensor<int32, [1]> input_axes_0 = const()[name = string("input_axes_0"), val = tensor<int32, [1]>([2])];
tensor<fp16, [1, 128]> input_cast_fp16 = squeeze(axes = input_axes_0, x = prelu_0_cast_fp16)[name = string("input_cast_fp16")];
tensor<fp16, [1, 128]> m_predictor_layers_1_weight_to_fp16 = const()[name = string("m_predictor_layers_1_weight_to_fp16"), val = tensor<fp16, [1, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1797632)))];
tensor<fp16, [1]> m_predictor_layers_1_bias_to_fp16 = const()[name = string("m_predictor_layers_1_bias_to_fp16"), val = tensor<fp16, [1]>([0x1.60cp-6])];
tensor<fp16, [1, 1]> linear_1_cast_fp16 = linear(bias = m_predictor_layers_1_bias_to_fp16, weight = m_predictor_layers_1_weight_to_fp16, x = input_cast_fp16)[name = string("linear_1_cast_fp16")];
tensor<fp16, [1, 1]> x_cast_fp16 = exp(x = linear_1_cast_fp16)[name = string("x_cast_fp16")];
tensor<int32, [1]> var_767_axes_0 = const()[name = string("op_767_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [1]> var_767_cast_fp16 = squeeze(axes = var_767_axes_0, x = x_cast_fp16)[name = string("op_767_cast_fp16")];
string var_767_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_767_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
tensor<fp32, [1]> duration = cast(dtype = var_767_cast_fp16_to_fp32_dtype_0, x = var_767_cast_fp16)[name = string("cast_20")];
} -> (duration);
} |