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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, 144, ?]> latent) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, list<tensor<int32, [2]>, ?>>>>((("DefaultShapes", {{"latent", [1, 144, 4]}}), ("RangeDims", {{"latent", [[1, 1], [144, 144], [4, 512]]}})))] {
string latent_to_fp16_dtype_0 = const()[name = string("latent_to_fp16_dtype_0"), val = string("fp16")];
fp16 _inversed_x_1_y_0_to_fp16 = const()[name = string("_inversed_x_1_y_0_to_fp16"), val = fp16(0x1p+2)];
tensor<fp16, [1, 144, ?]> latent_to_fp16 = cast(dtype = latent_to_fp16_dtype_0, x = latent)[name = string("cast_6")];
tensor<fp16, [1, 144, ?]> _inversed_x_1_cast_fp16 = mul(x = latent_to_fp16, y = _inversed_x_1_y_0_to_fp16)[name = string("_inversed_x_1_cast_fp16")];
tensor<int32, [4]> var_33 = const()[name = string("op_33"), val = tensor<int32, [4]>([1, 24, 6, -1])];
tensor<fp16, [1, 24, 6, ?]> x_3_cast_fp16 = reshape(shape = var_33, x = _inversed_x_1_cast_fp16)[name = string("x_3_cast_fp16")];
tensor<int32, [4]> var_37_perm_0 = const()[name = string("op_37_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
tensor<int32, [3]> var_43 = const()[name = string("op_43"), val = tensor<int32, [3]>([1, 24, -1])];
tensor<fp16, [1, 24, ?, 6]> var_37_cast_fp16 = transpose(perm = var_37_perm_0, x = x_3_cast_fp16)[name = string("transpose_21")];
tensor<fp16, [1, 24, ?]> x_7_cast_fp16 = reshape(shape = var_43, x = var_37_cast_fp16)[name = string("x_7_cast_fp16")];
tensor<fp16, [1, 24, 1]> latent_std_to_fp16 = const()[name = string("latent_std_to_fp16"), val = tensor<fp16, [1, 24, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
tensor<fp16, [1, 24, ?]> var_45_cast_fp16 = mul(x = x_7_cast_fp16, y = latent_std_to_fp16)[name = string("op_45_cast_fp16")];
tensor<fp16, [1, 24, 1]> latent_mean_to_fp16 = const()[name = string("latent_mean_to_fp16"), val = tensor<fp16, [1, 24, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192)))];
tensor<fp16, [1, 24, ?]> input_1_cast_fp16 = add(x = var_45_cast_fp16, y = latent_mean_to_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, 6, 0])];
string input_3_mode_0 = const()[name = string("input_3_mode_0"), val = string("replicate")];
fp16 const_0_to_fp16 = const()[name = string("const_0_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 24, ?]> input_3_cast_fp16 = pad(constant_val = const_0_to_fp16, mode = input_3_mode_0, pad = input_3_pad_0, x = input_1_cast_fp16)[name = string("input_3_cast_fp16")];
string input_5_pad_type_0 = const()[name = string("input_5_pad_type_0"), val = string("valid")];
tensor<int32, [1]> input_5_strides_0 = const()[name = string("input_5_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> input_5_dilations_0 = const()[name = string("input_5_dilations_0"), val = tensor<int32, [1]>([1])];
int32 input_5_groups_0 = const()[name = string("input_5_groups_0"), val = int32(1)];
tensor<fp16, [512, 24, 7]> embed_net_weight_to_fp16 = const()[name = string("embed_net_weight_to_fp16"), val = tensor<fp16, [512, 24, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(320)))];
tensor<fp16, [512]> embed_net_bias_to_fp16 = const()[name = string("embed_net_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(172416)))];
tensor<fp16, [1, 512, ?]> input_5_cast_fp16 = conv(bias = embed_net_bias_to_fp16, dilations = input_5_dilations_0, groups = input_5_groups_0, pad = input_5_pad_0, pad_type = input_5_pad_type_0, strides = input_5_strides_0, weight = embed_net_weight_to_fp16, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")];
tensor<int32, [6]> input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 6, 0])];
string input_7_mode_0 = const()[name = string("input_7_mode_0"), val = string("replicate")];
fp16 const_1_to_fp16 = const()[name = string("const_1_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 512, ?]> input_7_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = input_7_mode_0, pad = input_7_pad_0, x = input_5_cast_fp16)[name = string("input_7_cast_fp16")];
string x_9_pad_type_0 = const()[name = string("x_9_pad_type_0"), val = string("valid")];
int32 x_9_groups_0 = const()[name = string("x_9_groups_0"), val = int32(512)];
tensor<int32, [1]> x_9_strides_0 = const()[name = string("x_9_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> x_9_pad_0 = const()[name = string("x_9_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> x_9_dilations_0 = const()[name = string("x_9_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<fp16, [512, 1, 7]> convnext_0_dwconv_net_weight_to_fp16 = const()[name = string("convnext_0_dwconv_net_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(173504)))];
tensor<fp16, [512]> convnext_0_dwconv_net_bias_to_fp16 = const()[name = string("convnext_0_dwconv_net_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180736)))];
tensor<fp16, [1, 512, ?]> x_9_cast_fp16 = conv(bias = convnext_0_dwconv_net_bias_to_fp16, dilations = x_9_dilations_0, groups = x_9_groups_0, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = x_9_strides_0, weight = convnext_0_dwconv_net_weight_to_fp16, x = input_7_cast_fp16)[name = string("x_9_cast_fp16")];
tensor<int32, [3]> input_9_perm_0 = const()[name = string("input_9_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<int32, [1]> var_96_axes_0 = const()[name = string("op_96_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [512]> convnext_0_norm_norm_weight_to_fp16 = const()[name = string("convnext_0_norm_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181824)))];
tensor<fp16, [512]> convnext_0_norm_norm_bias_to_fp16 = const()[name = string("convnext_0_norm_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(182912)))];
fp16 var_67_to_fp16 = const()[name = string("op_67_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, ?, 512]> input_9_cast_fp16 = transpose(perm = input_9_perm_0, x = x_9_cast_fp16)[name = string("transpose_20")];
tensor<fp16, [1, ?, 512]> var_96_cast_fp16 = layer_norm(axes = var_96_axes_0, beta = convnext_0_norm_norm_bias_to_fp16, epsilon = var_67_to_fp16, gamma = convnext_0_norm_norm_weight_to_fp16, x = input_9_cast_fp16)[name = string("op_96_cast_fp16")];
tensor<int32, [3]> input_11_perm_0 = const()[name = string("input_11_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
string h_1_pad_type_0 = const()[name = string("h_1_pad_type_0"), val = string("valid")];
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])];
int32 h_1_groups_0 = const()[name = string("h_1_groups_0"), val = int32(1)];
tensor<fp16, [2048, 512, 1]> convnext_0_pwconv1_weight_to_fp16 = const()[name = string("convnext_0_pwconv1_weight_to_fp16"), val = tensor<fp16, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184000)))];
tensor<fp16, [2048]> convnext_0_pwconv1_bias_to_fp16 = const()[name = string("convnext_0_pwconv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2281216)))];
tensor<fp16, [1, 512, ?]> input_11_cast_fp16 = transpose(perm = input_11_perm_0, x = var_96_cast_fp16)[name = string("transpose_19")];
tensor<fp16, [1, 2048, ?]> h_1_cast_fp16 = conv(bias = convnext_0_pwconv1_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 = convnext_0_pwconv1_weight_to_fp16, x = input_11_cast_fp16)[name = string("h_1_cast_fp16")];
string input_13_mode_0 = const()[name = string("input_13_mode_0"), val = string("EXACT")];
tensor<fp16, [1, 2048, ?]> input_13_cast_fp16 = gelu(mode = input_13_mode_0, x = h_1_cast_fp16)[name = string("input_13_cast_fp16")];
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, [512, 2048, 1]> var_113_weight_0_to_fp16 = const()[name = string("op_113_weight_0_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2285376)))];
tensor<fp16, [512]> var_113_bias_0_to_fp16 = const()[name = string("op_113_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4382592)))];
tensor<fp16, [1, 512, ?]> var_113_cast_fp16 = conv(bias = var_113_bias_0_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 = var_113_weight_0_to_fp16, x = input_13_cast_fp16)[name = string("op_113_cast_fp16")];
tensor<fp16, [1, 512, ?]> input_15_cast_fp16 = add(x = input_5_cast_fp16, y = var_113_cast_fp16)[name = string("input_15_cast_fp16")];
tensor<int32, [6]> input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 12, 0])];
string input_17_mode_0 = const()[name = string("input_17_mode_0"), val = string("replicate")];
fp16 const_2_to_fp16 = const()[name = string("const_2_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 512, ?]> input_17_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = input_17_mode_0, pad = input_17_pad_0, x = input_15_cast_fp16)[name = string("input_17_cast_fp16")];
string x_11_pad_type_0 = const()[name = string("x_11_pad_type_0"), val = string("valid")];
tensor<int32, [1]> x_11_dilations_0 = const()[name = string("x_11_dilations_0"), val = tensor<int32, [1]>([2])];
int32 x_11_groups_0 = const()[name = string("x_11_groups_0"), val = int32(512)];
tensor<int32, [1]> x_11_strides_0 = const()[name = string("x_11_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> x_11_pad_0 = const()[name = string("x_11_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<fp16, [512, 1, 7]> convnext_1_dwconv_net_weight_to_fp16 = const()[name = string("convnext_1_dwconv_net_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4383680)))];
tensor<fp16, [512]> convnext_1_dwconv_net_bias_to_fp16 = const()[name = string("convnext_1_dwconv_net_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4390912)))];
tensor<fp16, [1, 512, ?]> x_11_cast_fp16 = conv(bias = convnext_1_dwconv_net_bias_to_fp16, dilations = x_11_dilations_0, groups = x_11_groups_0, pad = x_11_pad_0, pad_type = x_11_pad_type_0, strides = x_11_strides_0, weight = convnext_1_dwconv_net_weight_to_fp16, x = input_17_cast_fp16)[name = string("x_11_cast_fp16")];
tensor<int32, [3]> input_19_perm_0 = const()[name = string("input_19_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<int32, [1]> var_146_axes_0 = const()[name = string("op_146_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [512]> convnext_1_norm_norm_weight_to_fp16 = const()[name = string("convnext_1_norm_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4392000)))];
tensor<fp16, [512]> convnext_1_norm_norm_bias_to_fp16 = const()[name = string("convnext_1_norm_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4393088)))];
fp16 var_116_to_fp16 = const()[name = string("op_116_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, ?, 512]> input_19_cast_fp16 = transpose(perm = input_19_perm_0, x = x_11_cast_fp16)[name = string("transpose_18")];
tensor<fp16, [1, ?, 512]> var_146_cast_fp16 = layer_norm(axes = var_146_axes_0, beta = convnext_1_norm_norm_bias_to_fp16, epsilon = var_116_to_fp16, gamma = convnext_1_norm_norm_weight_to_fp16, x = input_19_cast_fp16)[name = string("op_146_cast_fp16")];
tensor<int32, [3]> input_21_perm_0 = const()[name = string("input_21_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
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, [2048, 512, 1]> convnext_1_pwconv1_weight_to_fp16 = const()[name = string("convnext_1_pwconv1_weight_to_fp16"), val = tensor<fp16, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4394176)))];
tensor<fp16, [2048]> convnext_1_pwconv1_bias_to_fp16 = const()[name = string("convnext_1_pwconv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6491392)))];
tensor<fp16, [1, 512, ?]> input_21_cast_fp16 = transpose(perm = input_21_perm_0, x = var_146_cast_fp16)[name = string("transpose_17")];
tensor<fp16, [1, 2048, ?]> h_5_cast_fp16 = conv(bias = convnext_1_pwconv1_bias_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 = convnext_1_pwconv1_weight_to_fp16, x = input_21_cast_fp16)[name = string("h_5_cast_fp16")];
string input_23_mode_0 = const()[name = string("input_23_mode_0"), val = string("EXACT")];
tensor<fp16, [1, 2048, ?]> input_23_cast_fp16 = gelu(mode = input_23_mode_0, x = h_5_cast_fp16)[name = string("input_23_cast_fp16")];
string h_7_pad_type_0 = const()[name = string("h_7_pad_type_0"), val = string("valid")];
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])];
int32 h_7_groups_0 = const()[name = string("h_7_groups_0"), val = int32(1)];
tensor<fp16, [512, 2048, 1]> var_163_weight_0_to_fp16 = const()[name = string("op_163_weight_0_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6495552)))];
tensor<fp16, [512]> var_163_bias_0_to_fp16 = const()[name = string("op_163_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8592768)))];
tensor<fp16, [1, 512, ?]> var_163_cast_fp16 = conv(bias = var_163_bias_0_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 = var_163_weight_0_to_fp16, x = input_23_cast_fp16)[name = string("op_163_cast_fp16")];
tensor<fp16, [1, 512, ?]> input_25_cast_fp16 = add(x = input_15_cast_fp16, y = var_163_cast_fp16)[name = string("input_25_cast_fp16")];
tensor<int32, [6]> input_27_pad_0 = const()[name = string("input_27_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 24, 0])];
string input_27_mode_0 = const()[name = string("input_27_mode_0"), val = string("replicate")];
fp16 const_3_to_fp16 = const()[name = string("const_3_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 512, ?]> input_27_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = input_27_mode_0, pad = input_27_pad_0, x = input_25_cast_fp16)[name = string("input_27_cast_fp16")];
string x_13_pad_type_0 = const()[name = string("x_13_pad_type_0"), val = string("valid")];
tensor<int32, [1]> x_13_dilations_0 = const()[name = string("x_13_dilations_0"), val = tensor<int32, [1]>([4])];
int32 x_13_groups_0 = const()[name = string("x_13_groups_0"), val = int32(512)];
tensor<int32, [1]> x_13_strides_0 = const()[name = string("x_13_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> x_13_pad_0 = const()[name = string("x_13_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<fp16, [512, 1, 7]> convnext_2_dwconv_net_weight_to_fp16 = const()[name = string("convnext_2_dwconv_net_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8593856)))];
tensor<fp16, [512]> convnext_2_dwconv_net_bias_to_fp16 = const()[name = string("convnext_2_dwconv_net_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8601088)))];
tensor<fp16, [1, 512, ?]> x_13_cast_fp16 = conv(bias = convnext_2_dwconv_net_bias_to_fp16, dilations = x_13_dilations_0, groups = x_13_groups_0, pad = x_13_pad_0, pad_type = x_13_pad_type_0, strides = x_13_strides_0, weight = convnext_2_dwconv_net_weight_to_fp16, x = input_27_cast_fp16)[name = string("x_13_cast_fp16")];
tensor<int32, [3]> input_29_perm_0 = const()[name = string("input_29_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<int32, [1]> var_197_axes_0 = const()[name = string("op_197_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [512]> convnext_2_norm_norm_weight_to_fp16 = const()[name = string("convnext_2_norm_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8602176)))];
tensor<fp16, [512]> convnext_2_norm_norm_bias_to_fp16 = const()[name = string("convnext_2_norm_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8603264)))];
fp16 var_167_to_fp16 = const()[name = string("op_167_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, ?, 512]> input_29_cast_fp16 = transpose(perm = input_29_perm_0, x = x_13_cast_fp16)[name = string("transpose_16")];
tensor<fp16, [1, ?, 512]> var_197_cast_fp16 = layer_norm(axes = var_197_axes_0, beta = convnext_2_norm_norm_bias_to_fp16, epsilon = var_167_to_fp16, gamma = convnext_2_norm_norm_weight_to_fp16, x = input_29_cast_fp16)[name = string("op_197_cast_fp16")];
tensor<int32, [3]> input_31_perm_0 = const()[name = string("input_31_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, [2048, 512, 1]> convnext_2_pwconv1_weight_to_fp16 = const()[name = string("convnext_2_pwconv1_weight_to_fp16"), val = tensor<fp16, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8604352)))];
tensor<fp16, [2048]> convnext_2_pwconv1_bias_to_fp16 = const()[name = string("convnext_2_pwconv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10701568)))];
tensor<fp16, [1, 512, ?]> input_31_cast_fp16 = transpose(perm = input_31_perm_0, x = var_197_cast_fp16)[name = string("transpose_15")];
tensor<fp16, [1, 2048, ?]> h_9_cast_fp16 = conv(bias = convnext_2_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 = convnext_2_pwconv1_weight_to_fp16, x = input_31_cast_fp16)[name = string("h_9_cast_fp16")];
string input_33_mode_0 = const()[name = string("input_33_mode_0"), val = string("EXACT")];
tensor<fp16, [1, 2048, ?]> input_33_cast_fp16 = gelu(mode = input_33_mode_0, x = h_9_cast_fp16)[name = string("input_33_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, [512, 2048, 1]> var_214_weight_0_to_fp16 = const()[name = string("op_214_weight_0_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10705728)))];
tensor<fp16, [512]> var_214_bias_0_to_fp16 = const()[name = string("op_214_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12802944)))];
tensor<fp16, [1, 512, ?]> var_214_cast_fp16 = conv(bias = var_214_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_214_weight_0_to_fp16, x = input_33_cast_fp16)[name = string("op_214_cast_fp16")];
tensor<fp16, [1, 512, ?]> input_35_cast_fp16 = add(x = input_25_cast_fp16, y = var_214_cast_fp16)[name = string("input_35_cast_fp16")];
tensor<int32, [6]> input_37_pad_0 = const()[name = string("input_37_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 6, 0])];
string input_37_mode_0 = const()[name = string("input_37_mode_0"), val = string("replicate")];
fp16 const_4_to_fp16 = const()[name = string("const_4_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 512, ?]> input_37_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = input_37_mode_0, pad = input_37_pad_0, x = input_35_cast_fp16)[name = string("input_37_cast_fp16")];
string x_15_pad_type_0 = const()[name = string("x_15_pad_type_0"), val = string("valid")];
int32 x_15_groups_0 = const()[name = string("x_15_groups_0"), val = int32(512)];
tensor<int32, [1]> x_15_strides_0 = const()[name = string("x_15_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> x_15_pad_0 = const()[name = string("x_15_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> x_15_dilations_0 = const()[name = string("x_15_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<fp16, [512, 1, 7]> convnext_3_dwconv_net_weight_to_fp16 = const()[name = string("convnext_3_dwconv_net_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12804032)))];
tensor<fp16, [512]> convnext_3_dwconv_net_bias_to_fp16 = const()[name = string("convnext_3_dwconv_net_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12811264)))];
tensor<fp16, [1, 512, ?]> x_15_cast_fp16 = conv(bias = convnext_3_dwconv_net_bias_to_fp16, dilations = x_15_dilations_0, groups = x_15_groups_0, pad = x_15_pad_0, pad_type = x_15_pad_type_0, strides = x_15_strides_0, weight = convnext_3_dwconv_net_weight_to_fp16, x = input_37_cast_fp16)[name = string("x_15_cast_fp16")];
tensor<int32, [3]> input_39_perm_0 = const()[name = string("input_39_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<int32, [1]> var_247_axes_0 = const()[name = string("op_247_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [512]> convnext_3_norm_norm_weight_to_fp16 = const()[name = string("convnext_3_norm_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12812352)))];
tensor<fp16, [512]> convnext_3_norm_norm_bias_to_fp16 = const()[name = string("convnext_3_norm_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12813440)))];
fp16 var_218_to_fp16 = const()[name = string("op_218_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, ?, 512]> input_39_cast_fp16 = transpose(perm = input_39_perm_0, x = x_15_cast_fp16)[name = string("transpose_14")];
tensor<fp16, [1, ?, 512]> var_247_cast_fp16 = layer_norm(axes = var_247_axes_0, beta = convnext_3_norm_norm_bias_to_fp16, epsilon = var_218_to_fp16, gamma = convnext_3_norm_norm_weight_to_fp16, x = input_39_cast_fp16)[name = string("op_247_cast_fp16")];
tensor<int32, [3]> input_41_perm_0 = const()[name = string("input_41_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
string h_13_pad_type_0 = const()[name = string("h_13_pad_type_0"), val = string("valid")];
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])];
int32 h_13_groups_0 = const()[name = string("h_13_groups_0"), val = int32(1)];
tensor<fp16, [2048, 512, 1]> convnext_3_pwconv1_weight_to_fp16 = const()[name = string("convnext_3_pwconv1_weight_to_fp16"), val = tensor<fp16, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12814528)))];
tensor<fp16, [2048]> convnext_3_pwconv1_bias_to_fp16 = const()[name = string("convnext_3_pwconv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14911744)))];
tensor<fp16, [1, 512, ?]> input_41_cast_fp16 = transpose(perm = input_41_perm_0, x = var_247_cast_fp16)[name = string("transpose_13")];
tensor<fp16, [1, 2048, ?]> h_13_cast_fp16 = conv(bias = convnext_3_pwconv1_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 = convnext_3_pwconv1_weight_to_fp16, x = input_41_cast_fp16)[name = string("h_13_cast_fp16")];
string input_43_mode_0 = const()[name = string("input_43_mode_0"), val = string("EXACT")];
tensor<fp16, [1, 2048, ?]> input_43_cast_fp16 = gelu(mode = input_43_mode_0, x = h_13_cast_fp16)[name = string("input_43_cast_fp16")];
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, [512, 2048, 1]> var_264_weight_0_to_fp16 = const()[name = string("op_264_weight_0_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14915904)))];
tensor<fp16, [512]> var_264_bias_0_to_fp16 = const()[name = string("op_264_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17013120)))];
tensor<fp16, [1, 512, ?]> var_264_cast_fp16 = conv(bias = var_264_bias_0_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 = var_264_weight_0_to_fp16, x = input_43_cast_fp16)[name = string("op_264_cast_fp16")];
tensor<fp16, [1, 512, ?]> input_45_cast_fp16 = add(x = input_35_cast_fp16, y = var_264_cast_fp16)[name = string("input_45_cast_fp16")];
tensor<int32, [6]> input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 12, 0])];
string input_47_mode_0 = const()[name = string("input_47_mode_0"), val = string("replicate")];
fp16 const_5_to_fp16 = const()[name = string("const_5_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 512, ?]> input_47_cast_fp16 = pad(constant_val = const_5_to_fp16, mode = input_47_mode_0, pad = input_47_pad_0, x = input_45_cast_fp16)[name = string("input_47_cast_fp16")];
string x_17_pad_type_0 = const()[name = string("x_17_pad_type_0"), val = string("valid")];
tensor<int32, [1]> x_17_dilations_0 = const()[name = string("x_17_dilations_0"), val = tensor<int32, [1]>([2])];
int32 x_17_groups_0 = const()[name = string("x_17_groups_0"), val = int32(512)];
tensor<int32, [1]> x_17_strides_0 = const()[name = string("x_17_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> x_17_pad_0 = const()[name = string("x_17_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<fp16, [512, 1, 7]> convnext_4_dwconv_net_weight_to_fp16 = const()[name = string("convnext_4_dwconv_net_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17014208)))];
tensor<fp16, [512]> convnext_4_dwconv_net_bias_to_fp16 = const()[name = string("convnext_4_dwconv_net_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17021440)))];
tensor<fp16, [1, 512, ?]> x_17_cast_fp16 = conv(bias = convnext_4_dwconv_net_bias_to_fp16, dilations = x_17_dilations_0, groups = x_17_groups_0, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = x_17_strides_0, weight = convnext_4_dwconv_net_weight_to_fp16, x = input_47_cast_fp16)[name = string("x_17_cast_fp16")];
tensor<int32, [3]> input_49_perm_0 = const()[name = string("input_49_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<int32, [1]> var_297_axes_0 = const()[name = string("op_297_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [512]> convnext_4_norm_norm_weight_to_fp16 = const()[name = string("convnext_4_norm_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17022528)))];
tensor<fp16, [512]> convnext_4_norm_norm_bias_to_fp16 = const()[name = string("convnext_4_norm_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17023616)))];
fp16 var_267_to_fp16 = const()[name = string("op_267_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, ?, 512]> input_49_cast_fp16 = transpose(perm = input_49_perm_0, x = x_17_cast_fp16)[name = string("transpose_12")];
tensor<fp16, [1, ?, 512]> var_297_cast_fp16 = layer_norm(axes = var_297_axes_0, beta = convnext_4_norm_norm_bias_to_fp16, epsilon = var_267_to_fp16, gamma = convnext_4_norm_norm_weight_to_fp16, x = input_49_cast_fp16)[name = string("op_297_cast_fp16")];
tensor<int32, [3]> input_51_perm_0 = const()[name = string("input_51_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
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, [2048, 512, 1]> convnext_4_pwconv1_weight_to_fp16 = const()[name = string("convnext_4_pwconv1_weight_to_fp16"), val = tensor<fp16, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17024704)))];
tensor<fp16, [2048]> convnext_4_pwconv1_bias_to_fp16 = const()[name = string("convnext_4_pwconv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19121920)))];
tensor<fp16, [1, 512, ?]> input_51_cast_fp16 = transpose(perm = input_51_perm_0, x = var_297_cast_fp16)[name = string("transpose_11")];
tensor<fp16, [1, 2048, ?]> h_17_cast_fp16 = conv(bias = convnext_4_pwconv1_bias_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 = convnext_4_pwconv1_weight_to_fp16, x = input_51_cast_fp16)[name = string("h_17_cast_fp16")];
string input_53_mode_0 = const()[name = string("input_53_mode_0"), val = string("EXACT")];
tensor<fp16, [1, 2048, ?]> input_53_cast_fp16 = gelu(mode = input_53_mode_0, x = h_17_cast_fp16)[name = string("input_53_cast_fp16")];
string h_19_pad_type_0 = const()[name = string("h_19_pad_type_0"), val = string("valid")];
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])];
int32 h_19_groups_0 = const()[name = string("h_19_groups_0"), val = int32(1)];
tensor<fp16, [512, 2048, 1]> var_314_weight_0_to_fp16 = const()[name = string("op_314_weight_0_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19126080)))];
tensor<fp16, [512]> var_314_bias_0_to_fp16 = const()[name = string("op_314_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21223296)))];
tensor<fp16, [1, 512, ?]> var_314_cast_fp16 = conv(bias = var_314_bias_0_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 = var_314_weight_0_to_fp16, x = input_53_cast_fp16)[name = string("op_314_cast_fp16")];
tensor<fp16, [1, 512, ?]> input_55_cast_fp16 = add(x = input_45_cast_fp16, y = var_314_cast_fp16)[name = string("input_55_cast_fp16")];
tensor<int32, [6]> input_57_pad_0 = const()[name = string("input_57_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 24, 0])];
string input_57_mode_0 = const()[name = string("input_57_mode_0"), val = string("replicate")];
fp16 const_6_to_fp16 = const()[name = string("const_6_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 512, ?]> input_57_cast_fp16 = pad(constant_val = const_6_to_fp16, mode = input_57_mode_0, pad = input_57_pad_0, x = input_55_cast_fp16)[name = string("input_57_cast_fp16")];
string x_19_pad_type_0 = const()[name = string("x_19_pad_type_0"), val = string("valid")];
tensor<int32, [1]> x_19_dilations_0 = const()[name = string("x_19_dilations_0"), val = tensor<int32, [1]>([4])];
int32 x_19_groups_0 = const()[name = string("x_19_groups_0"), val = int32(512)];
tensor<int32, [1]> x_19_strides_0 = const()[name = string("x_19_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> x_19_pad_0 = const()[name = string("x_19_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<fp16, [512, 1, 7]> convnext_5_dwconv_net_weight_to_fp16 = const()[name = string("convnext_5_dwconv_net_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21224384)))];
tensor<fp16, [512]> convnext_5_dwconv_net_bias_to_fp16 = const()[name = string("convnext_5_dwconv_net_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21231616)))];
tensor<fp16, [1, 512, ?]> x_19_cast_fp16 = conv(bias = convnext_5_dwconv_net_bias_to_fp16, dilations = x_19_dilations_0, groups = x_19_groups_0, pad = x_19_pad_0, pad_type = x_19_pad_type_0, strides = x_19_strides_0, weight = convnext_5_dwconv_net_weight_to_fp16, x = input_57_cast_fp16)[name = string("x_19_cast_fp16")];
tensor<int32, [3]> input_59_perm_0 = const()[name = string("input_59_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<int32, [1]> var_348_axes_0 = const()[name = string("op_348_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [512]> convnext_5_norm_norm_weight_to_fp16 = const()[name = string("convnext_5_norm_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21232704)))];
tensor<fp16, [512]> convnext_5_norm_norm_bias_to_fp16 = const()[name = string("convnext_5_norm_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21233792)))];
fp16 var_318_to_fp16 = const()[name = string("op_318_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, ?, 512]> input_59_cast_fp16 = transpose(perm = input_59_perm_0, x = x_19_cast_fp16)[name = string("transpose_10")];
tensor<fp16, [1, ?, 512]> var_348_cast_fp16 = layer_norm(axes = var_348_axes_0, beta = convnext_5_norm_norm_bias_to_fp16, epsilon = var_318_to_fp16, gamma = convnext_5_norm_norm_weight_to_fp16, x = input_59_cast_fp16)[name = string("op_348_cast_fp16")];
tensor<int32, [3]> input_61_perm_0 = const()[name = string("input_61_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, [2048, 512, 1]> convnext_5_pwconv1_weight_to_fp16 = const()[name = string("convnext_5_pwconv1_weight_to_fp16"), val = tensor<fp16, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21234880)))];
tensor<fp16, [2048]> convnext_5_pwconv1_bias_to_fp16 = const()[name = string("convnext_5_pwconv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23332096)))];
tensor<fp16, [1, 512, ?]> input_61_cast_fp16 = transpose(perm = input_61_perm_0, x = var_348_cast_fp16)[name = string("transpose_9")];
tensor<fp16, [1, 2048, ?]> h_21_cast_fp16 = conv(bias = convnext_5_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 = convnext_5_pwconv1_weight_to_fp16, x = input_61_cast_fp16)[name = string("h_21_cast_fp16")];
string input_63_mode_0 = const()[name = string("input_63_mode_0"), val = string("EXACT")];
tensor<fp16, [1, 2048, ?]> input_63_cast_fp16 = gelu(mode = input_63_mode_0, x = h_21_cast_fp16)[name = string("input_63_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, [512, 2048, 1]> var_365_weight_0_to_fp16 = const()[name = string("op_365_weight_0_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23336256)))];
tensor<fp16, [512]> var_365_bias_0_to_fp16 = const()[name = string("op_365_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25433472)))];
tensor<fp16, [1, 512, ?]> var_365_cast_fp16 = conv(bias = var_365_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_365_weight_0_to_fp16, x = input_63_cast_fp16)[name = string("op_365_cast_fp16")];
tensor<fp16, [1, 512, ?]> input_65_cast_fp16 = add(x = input_55_cast_fp16, y = var_365_cast_fp16)[name = string("input_65_cast_fp16")];
tensor<int32, [6]> input_67_pad_0 = const()[name = string("input_67_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 6, 0])];
string input_67_mode_0 = const()[name = string("input_67_mode_0"), val = string("replicate")];
fp16 const_7_to_fp16 = const()[name = string("const_7_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 512, ?]> input_67_cast_fp16 = pad(constant_val = const_7_to_fp16, mode = input_67_mode_0, pad = input_67_pad_0, x = input_65_cast_fp16)[name = string("input_67_cast_fp16")];
string x_21_pad_type_0 = const()[name = string("x_21_pad_type_0"), val = string("valid")];
int32 x_21_groups_0 = const()[name = string("x_21_groups_0"), val = int32(512)];
tensor<int32, [1]> x_21_strides_0 = const()[name = string("x_21_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> x_21_pad_0 = const()[name = string("x_21_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> x_21_dilations_0 = const()[name = string("x_21_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<fp16, [512, 1, 7]> convnext_6_dwconv_net_weight_to_fp16 = const()[name = string("convnext_6_dwconv_net_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25434560)))];
tensor<fp16, [512]> convnext_6_dwconv_net_bias_to_fp16 = const()[name = string("convnext_6_dwconv_net_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25441792)))];
tensor<fp16, [1, 512, ?]> x_21_cast_fp16 = conv(bias = convnext_6_dwconv_net_bias_to_fp16, dilations = x_21_dilations_0, groups = x_21_groups_0, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = x_21_strides_0, weight = convnext_6_dwconv_net_weight_to_fp16, x = input_67_cast_fp16)[name = string("x_21_cast_fp16")];
tensor<int32, [3]> input_69_perm_0 = const()[name = string("input_69_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<int32, [1]> var_398_axes_0 = const()[name = string("op_398_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [512]> convnext_6_norm_norm_weight_to_fp16 = const()[name = string("convnext_6_norm_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25442880)))];
tensor<fp16, [512]> convnext_6_norm_norm_bias_to_fp16 = const()[name = string("convnext_6_norm_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25443968)))];
fp16 var_369_to_fp16 = const()[name = string("op_369_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, ?, 512]> input_69_cast_fp16 = transpose(perm = input_69_perm_0, x = x_21_cast_fp16)[name = string("transpose_8")];
tensor<fp16, [1, ?, 512]> var_398_cast_fp16 = layer_norm(axes = var_398_axes_0, beta = convnext_6_norm_norm_bias_to_fp16, epsilon = var_369_to_fp16, gamma = convnext_6_norm_norm_weight_to_fp16, x = input_69_cast_fp16)[name = string("op_398_cast_fp16")];
tensor<int32, [3]> input_71_perm_0 = const()[name = string("input_71_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
string h_25_pad_type_0 = const()[name = string("h_25_pad_type_0"), val = string("valid")];
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])];
int32 h_25_groups_0 = const()[name = string("h_25_groups_0"), val = int32(1)];
tensor<fp16, [2048, 512, 1]> convnext_6_pwconv1_weight_to_fp16 = const()[name = string("convnext_6_pwconv1_weight_to_fp16"), val = tensor<fp16, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25445056)))];
tensor<fp16, [2048]> convnext_6_pwconv1_bias_to_fp16 = const()[name = string("convnext_6_pwconv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27542272)))];
tensor<fp16, [1, 512, ?]> input_71_cast_fp16 = transpose(perm = input_71_perm_0, x = var_398_cast_fp16)[name = string("transpose_7")];
tensor<fp16, [1, 2048, ?]> h_25_cast_fp16 = conv(bias = convnext_6_pwconv1_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 = convnext_6_pwconv1_weight_to_fp16, x = input_71_cast_fp16)[name = string("h_25_cast_fp16")];
string input_73_mode_0 = const()[name = string("input_73_mode_0"), val = string("EXACT")];
tensor<fp16, [1, 2048, ?]> input_73_cast_fp16 = gelu(mode = input_73_mode_0, x = h_25_cast_fp16)[name = string("input_73_cast_fp16")];
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, [512, 2048, 1]> var_415_weight_0_to_fp16 = const()[name = string("op_415_weight_0_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27546432)))];
tensor<fp16, [512]> var_415_bias_0_to_fp16 = const()[name = string("op_415_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29643648)))];
tensor<fp16, [1, 512, ?]> var_415_cast_fp16 = conv(bias = var_415_bias_0_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 = var_415_weight_0_to_fp16, x = input_73_cast_fp16)[name = string("op_415_cast_fp16")];
tensor<fp16, [1, 512, ?]> input_75_cast_fp16 = add(x = input_65_cast_fp16, y = var_415_cast_fp16)[name = string("input_75_cast_fp16")];
tensor<int32, [6]> input_77_pad_0 = const()[name = string("input_77_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 6, 0])];
string input_77_mode_0 = const()[name = string("input_77_mode_0"), val = string("replicate")];
fp16 const_8_to_fp16 = const()[name = string("const_8_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 512, ?]> input_77_cast_fp16 = pad(constant_val = const_8_to_fp16, mode = input_77_mode_0, pad = input_77_pad_0, x = input_75_cast_fp16)[name = string("input_77_cast_fp16")];
string x_23_pad_type_0 = const()[name = string("x_23_pad_type_0"), val = string("valid")];
int32 x_23_groups_0 = const()[name = string("x_23_groups_0"), val = int32(512)];
tensor<int32, [1]> x_23_strides_0 = const()[name = string("x_23_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> x_23_pad_0 = const()[name = string("x_23_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> x_23_dilations_0 = const()[name = string("x_23_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<fp16, [512, 1, 7]> convnext_7_dwconv_net_weight_to_fp16 = const()[name = string("convnext_7_dwconv_net_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29644736)))];
tensor<fp16, [512]> convnext_7_dwconv_net_bias_to_fp16 = const()[name = string("convnext_7_dwconv_net_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29651968)))];
tensor<fp16, [1, 512, ?]> x_23_cast_fp16 = conv(bias = convnext_7_dwconv_net_bias_to_fp16, dilations = x_23_dilations_0, groups = x_23_groups_0, pad = x_23_pad_0, pad_type = x_23_pad_type_0, strides = x_23_strides_0, weight = convnext_7_dwconv_net_weight_to_fp16, x = input_77_cast_fp16)[name = string("x_23_cast_fp16")];
tensor<int32, [3]> input_79_perm_0 = const()[name = string("input_79_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<int32, [1]> var_448_axes_0 = const()[name = string("op_448_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [512]> convnext_7_norm_norm_weight_to_fp16 = const()[name = string("convnext_7_norm_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29653056)))];
tensor<fp16, [512]> convnext_7_norm_norm_bias_to_fp16 = const()[name = string("convnext_7_norm_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29654144)))];
fp16 var_419_to_fp16 = const()[name = string("op_419_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, ?, 512]> input_79_cast_fp16 = transpose(perm = input_79_perm_0, x = x_23_cast_fp16)[name = string("transpose_6")];
tensor<fp16, [1, ?, 512]> var_448_cast_fp16 = layer_norm(axes = var_448_axes_0, beta = convnext_7_norm_norm_bias_to_fp16, epsilon = var_419_to_fp16, gamma = convnext_7_norm_norm_weight_to_fp16, x = input_79_cast_fp16)[name = string("op_448_cast_fp16")];
tensor<int32, [3]> input_81_perm_0 = const()[name = string("input_81_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
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, [2048, 512, 1]> convnext_7_pwconv1_weight_to_fp16 = const()[name = string("convnext_7_pwconv1_weight_to_fp16"), val = tensor<fp16, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(29655232)))];
tensor<fp16, [2048]> convnext_7_pwconv1_bias_to_fp16 = const()[name = string("convnext_7_pwconv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31752448)))];
tensor<fp16, [1, 512, ?]> input_81_cast_fp16 = transpose(perm = input_81_perm_0, x = var_448_cast_fp16)[name = string("transpose_5")];
tensor<fp16, [1, 2048, ?]> h_29_cast_fp16 = conv(bias = convnext_7_pwconv1_bias_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 = convnext_7_pwconv1_weight_to_fp16, x = input_81_cast_fp16)[name = string("h_29_cast_fp16")];
string input_83_mode_0 = const()[name = string("input_83_mode_0"), val = string("EXACT")];
tensor<fp16, [1, 2048, ?]> input_83_cast_fp16 = gelu(mode = input_83_mode_0, x = h_29_cast_fp16)[name = string("input_83_cast_fp16")];
string h_31_pad_type_0 = const()[name = string("h_31_pad_type_0"), val = string("valid")];
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])];
int32 h_31_groups_0 = const()[name = string("h_31_groups_0"), val = int32(1)];
tensor<fp16, [512, 2048, 1]> var_465_weight_0_to_fp16 = const()[name = string("op_465_weight_0_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(31756608)))];
tensor<fp16, [512]> var_465_bias_0_to_fp16 = const()[name = string("op_465_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33853824)))];
tensor<fp16, [1, 512, ?]> var_465_cast_fp16 = conv(bias = var_465_bias_0_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 = var_465_weight_0_to_fp16, x = input_83_cast_fp16)[name = string("op_465_cast_fp16")];
tensor<fp16, [1, 512, ?]> input_85_cast_fp16 = add(x = input_75_cast_fp16, y = var_465_cast_fp16)[name = string("input_85_cast_fp16")];
tensor<int32, [6]> input_87_pad_0 = const()[name = string("input_87_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 6, 0])];
string input_87_mode_0 = const()[name = string("input_87_mode_0"), val = string("replicate")];
fp16 const_9_to_fp16 = const()[name = string("const_9_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 512, ?]> input_87_cast_fp16 = pad(constant_val = const_9_to_fp16, mode = input_87_mode_0, pad = input_87_pad_0, x = input_85_cast_fp16)[name = string("input_87_cast_fp16")];
string x_25_pad_type_0 = const()[name = string("x_25_pad_type_0"), val = string("valid")];
int32 x_25_groups_0 = const()[name = string("x_25_groups_0"), val = int32(512)];
tensor<int32, [1]> x_25_strides_0 = const()[name = string("x_25_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> x_25_pad_0 = const()[name = string("x_25_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> x_25_dilations_0 = const()[name = string("x_25_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<fp16, [512, 1, 7]> convnext_8_dwconv_net_weight_to_fp16 = const()[name = string("convnext_8_dwconv_net_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33854912)))];
tensor<fp16, [512]> convnext_8_dwconv_net_bias_to_fp16 = const()[name = string("convnext_8_dwconv_net_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33862144)))];
tensor<fp16, [1, 512, ?]> x_25_cast_fp16 = conv(bias = convnext_8_dwconv_net_bias_to_fp16, dilations = x_25_dilations_0, groups = x_25_groups_0, pad = x_25_pad_0, pad_type = x_25_pad_type_0, strides = x_25_strides_0, weight = convnext_8_dwconv_net_weight_to_fp16, x = input_87_cast_fp16)[name = string("x_25_cast_fp16")];
tensor<int32, [3]> input_89_perm_0 = const()[name = string("input_89_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<int32, [1]> var_498_axes_0 = const()[name = string("op_498_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [512]> convnext_8_norm_norm_weight_to_fp16 = const()[name = string("convnext_8_norm_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33863232)))];
tensor<fp16, [512]> convnext_8_norm_norm_bias_to_fp16 = const()[name = string("convnext_8_norm_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33864320)))];
fp16 var_469_to_fp16 = const()[name = string("op_469_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, ?, 512]> input_89_cast_fp16 = transpose(perm = input_89_perm_0, x = x_25_cast_fp16)[name = string("transpose_4")];
tensor<fp16, [1, ?, 512]> var_498_cast_fp16 = layer_norm(axes = var_498_axes_0, beta = convnext_8_norm_norm_bias_to_fp16, epsilon = var_469_to_fp16, gamma = convnext_8_norm_norm_weight_to_fp16, x = input_89_cast_fp16)[name = string("op_498_cast_fp16")];
tensor<int32, [3]> input_91_perm_0 = const()[name = string("input_91_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, [2048, 512, 1]> convnext_8_pwconv1_weight_to_fp16 = const()[name = string("convnext_8_pwconv1_weight_to_fp16"), val = tensor<fp16, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(33865408)))];
tensor<fp16, [2048]> convnext_8_pwconv1_bias_to_fp16 = const()[name = string("convnext_8_pwconv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35962624)))];
tensor<fp16, [1, 512, ?]> input_91_cast_fp16 = transpose(perm = input_91_perm_0, x = var_498_cast_fp16)[name = string("transpose_3")];
tensor<fp16, [1, 2048, ?]> h_33_cast_fp16 = conv(bias = convnext_8_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 = convnext_8_pwconv1_weight_to_fp16, x = input_91_cast_fp16)[name = string("h_33_cast_fp16")];
string input_93_mode_0 = const()[name = string("input_93_mode_0"), val = string("EXACT")];
tensor<fp16, [1, 2048, ?]> input_93_cast_fp16 = gelu(mode = input_93_mode_0, x = h_33_cast_fp16)[name = string("input_93_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, [512, 2048, 1]> var_515_weight_0_to_fp16 = const()[name = string("op_515_weight_0_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(35966784)))];
tensor<fp16, [512]> var_515_bias_0_to_fp16 = const()[name = string("op_515_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38064000)))];
tensor<fp16, [1, 512, ?]> var_515_cast_fp16 = conv(bias = var_515_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_515_weight_0_to_fp16, x = input_93_cast_fp16)[name = string("op_515_cast_fp16")];
tensor<fp16, [1, 512, ?]> input_95_cast_fp16 = add(x = input_85_cast_fp16, y = var_515_cast_fp16)[name = string("input_95_cast_fp16")];
tensor<int32, [6]> input_97_pad_0 = const()[name = string("input_97_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 6, 0])];
string input_97_mode_0 = const()[name = string("input_97_mode_0"), val = string("replicate")];
fp16 const_10_to_fp16 = const()[name = string("const_10_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 512, ?]> input_97_cast_fp16 = pad(constant_val = const_10_to_fp16, mode = input_97_mode_0, pad = input_97_pad_0, x = input_95_cast_fp16)[name = string("input_97_cast_fp16")];
string x_27_pad_type_0 = const()[name = string("x_27_pad_type_0"), val = string("valid")];
int32 x_27_groups_0 = const()[name = string("x_27_groups_0"), val = int32(512)];
tensor<int32, [1]> x_27_strides_0 = const()[name = string("x_27_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> x_27_pad_0 = const()[name = string("x_27_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> x_27_dilations_0 = const()[name = string("x_27_dilations_0"), val = tensor<int32, [1]>([1])];
tensor<fp16, [512, 1, 7]> convnext_9_dwconv_net_weight_to_fp16 = const()[name = string("convnext_9_dwconv_net_weight_to_fp16"), val = tensor<fp16, [512, 1, 7]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38065088)))];
tensor<fp16, [512]> convnext_9_dwconv_net_bias_to_fp16 = const()[name = string("convnext_9_dwconv_net_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38072320)))];
tensor<fp16, [1, 512, ?]> x_27_cast_fp16 = conv(bias = convnext_9_dwconv_net_bias_to_fp16, dilations = x_27_dilations_0, groups = x_27_groups_0, pad = x_27_pad_0, pad_type = x_27_pad_type_0, strides = x_27_strides_0, weight = convnext_9_dwconv_net_weight_to_fp16, x = input_97_cast_fp16)[name = string("x_27_cast_fp16")];
tensor<int32, [3]> input_99_perm_0 = const()[name = string("input_99_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<int32, [1]> var_548_axes_0 = const()[name = string("op_548_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<fp16, [512]> convnext_9_norm_norm_weight_to_fp16 = const()[name = string("convnext_9_norm_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38073408)))];
tensor<fp16, [512]> convnext_9_norm_norm_bias_to_fp16 = const()[name = string("convnext_9_norm_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38074496)))];
fp16 var_519_to_fp16 = const()[name = string("op_519_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, ?, 512]> input_99_cast_fp16 = transpose(perm = input_99_perm_0, x = x_27_cast_fp16)[name = string("transpose_2")];
tensor<fp16, [1, ?, 512]> var_548_cast_fp16 = layer_norm(axes = var_548_axes_0, beta = convnext_9_norm_norm_bias_to_fp16, epsilon = var_519_to_fp16, gamma = convnext_9_norm_norm_weight_to_fp16, x = input_99_cast_fp16)[name = string("op_548_cast_fp16")];
tensor<int32, [3]> input_101_perm_0 = const()[name = string("input_101_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
string h_37_pad_type_0 = const()[name = string("h_37_pad_type_0"), val = string("valid")];
tensor<int32, [1]> h_37_strides_0 = const()[name = string("h_37_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> h_37_pad_0 = const()[name = string("h_37_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> h_37_dilations_0 = const()[name = string("h_37_dilations_0"), val = tensor<int32, [1]>([1])];
int32 h_37_groups_0 = const()[name = string("h_37_groups_0"), val = int32(1)];
tensor<fp16, [2048, 512, 1]> convnext_9_pwconv1_weight_to_fp16 = const()[name = string("convnext_9_pwconv1_weight_to_fp16"), val = tensor<fp16, [2048, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38075584)))];
tensor<fp16, [2048]> convnext_9_pwconv1_bias_to_fp16 = const()[name = string("convnext_9_pwconv1_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40172800)))];
tensor<fp16, [1, 512, ?]> input_101_cast_fp16 = transpose(perm = input_101_perm_0, x = var_548_cast_fp16)[name = string("transpose_1")];
tensor<fp16, [1, 2048, ?]> h_37_cast_fp16 = conv(bias = convnext_9_pwconv1_bias_to_fp16, dilations = h_37_dilations_0, groups = h_37_groups_0, pad = h_37_pad_0, pad_type = h_37_pad_type_0, strides = h_37_strides_0, weight = convnext_9_pwconv1_weight_to_fp16, x = input_101_cast_fp16)[name = string("h_37_cast_fp16")];
string input_103_mode_0 = const()[name = string("input_103_mode_0"), val = string("EXACT")];
tensor<fp16, [1, 2048, ?]> input_103_cast_fp16 = gelu(mode = input_103_mode_0, x = h_37_cast_fp16)[name = string("input_103_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, [512, 2048, 1]> var_565_weight_0_to_fp16 = const()[name = string("op_565_weight_0_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40176960)))];
tensor<fp16, [512]> var_565_bias_0_to_fp16 = const()[name = string("op_565_bias_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42274176)))];
tensor<fp16, [1, 512, ?]> var_565_cast_fp16 = conv(bias = var_565_bias_0_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 = var_565_weight_0_to_fp16, x = input_103_cast_fp16)[name = string("op_565_cast_fp16")];
tensor<fp16, [1, 512, ?]> input_105_cast_fp16 = add(x = input_95_cast_fp16, y = var_565_cast_fp16)[name = string("input_105_cast_fp16")];
tensor<fp16, [512]> final_norm_norm_running_mean_to_fp16 = const()[name = string("final_norm_norm_running_mean_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42275264)))];
tensor<fp16, [512]> final_norm_norm_running_var_to_fp16 = const()[name = string("final_norm_norm_running_var_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42276352)))];
tensor<fp16, [512]> final_norm_norm_weight_to_fp16 = const()[name = string("final_norm_norm_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42277440)))];
tensor<fp16, [512]> final_norm_norm_bias_to_fp16 = const()[name = string("final_norm_norm_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42278528)))];
fp16 var_569_to_fp16 = const()[name = string("op_569_to_fp16"), val = fp16(0x1.5p-17)];
tensor<fp16, [1, 512, ?]> input_107_cast_fp16 = batch_norm(beta = final_norm_norm_bias_to_fp16, epsilon = var_569_to_fp16, gamma = final_norm_norm_weight_to_fp16, mean = final_norm_norm_running_mean_to_fp16, variance = final_norm_norm_running_var_to_fp16, x = input_105_cast_fp16)[name = string("input_107_cast_fp16")];
tensor<int32, [6]> input_109_pad_0 = const()[name = string("input_109_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 2, 0])];
string input_109_mode_0 = const()[name = string("input_109_mode_0"), val = string("replicate")];
fp16 const_11_to_fp16 = const()[name = string("const_11_to_fp16"), val = fp16(0x0p+0)];
tensor<fp16, [1, 512, ?]> input_109_cast_fp16 = pad(constant_val = const_11_to_fp16, mode = input_109_mode_0, pad = input_109_pad_0, x = input_107_cast_fp16)[name = string("input_109_cast_fp16")];
string input_111_pad_type_0 = const()[name = string("input_111_pad_type_0"), val = string("valid")];
tensor<int32, [1]> input_111_strides_0 = const()[name = string("input_111_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> input_111_pad_0 = const()[name = string("input_111_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> input_111_dilations_0 = const()[name = string("input_111_dilations_0"), val = tensor<int32, [1]>([1])];
int32 input_111_groups_0 = const()[name = string("input_111_groups_0"), val = int32(1)];
tensor<fp16, [2048, 512, 3]> head_layer1_net_weight_to_fp16 = const()[name = string("head_layer1_net_weight_to_fp16"), val = tensor<fp16, [2048, 512, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42279616)))];
tensor<fp16, [2048]> head_layer1_net_bias_to_fp16 = const()[name = string("head_layer1_net_bias_to_fp16"), val = tensor<fp16, [2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48571136)))];
tensor<fp16, [1, 2048, ?]> input_111_cast_fp16 = conv(bias = head_layer1_net_bias_to_fp16, dilations = input_111_dilations_0, groups = input_111_groups_0, pad = input_111_pad_0, pad_type = input_111_pad_type_0, strides = input_111_strides_0, weight = head_layer1_net_weight_to_fp16, x = input_109_cast_fp16)[name = string("input_111_cast_fp16")];
fp32 input_alpha_1 = const()[name = string("input_alpha_1"), val = fp32(0x1.9814fap-13)];
tensor<fp16, [1, 2048, ?]> input_cast_fp16 = leaky_relu(alpha = input_alpha_1, x = input_111_cast_fp16)[name = string("input_cast_fp16")];
string x_29_pad_type_0 = const()[name = string("x_29_pad_type_0"), val = string("valid")];
tensor<int32, [1]> x_29_strides_0 = const()[name = string("x_29_strides_0"), val = tensor<int32, [1]>([1])];
tensor<int32, [2]> x_29_pad_0 = const()[name = string("x_29_pad_0"), val = tensor<int32, [2]>([0, 0])];
tensor<int32, [1]> x_29_dilations_0 = const()[name = string("x_29_dilations_0"), val = tensor<int32, [1]>([1])];
int32 x_29_groups_0 = const()[name = string("x_29_groups_0"), val = int32(1)];
tensor<fp16, [512, 2048, 1]> head_layer2_weight_to_fp16 = const()[name = string("head_layer2_weight_to_fp16"), val = tensor<fp16, [512, 2048, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48575296)))];
tensor<fp16, [1, 512, ?]> x_29_cast_fp16 = conv(dilations = x_29_dilations_0, groups = x_29_groups_0, pad = x_29_pad_0, pad_type = x_29_pad_type_0, strides = x_29_strides_0, weight = head_layer2_weight_to_fp16, x = input_cast_fp16)[name = string("x_29_cast_fp16")];
tensor<int32, [3]> var_607_perm_0 = const()[name = string("op_607_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
tensor<int32, [2]> var_612 = const()[name = string("op_612"), val = tensor<int32, [2]>([1, -1])];
tensor<fp16, [1, ?, 512]> var_607_cast_fp16 = transpose(perm = var_607_perm_0, x = x_29_cast_fp16)[name = string("transpose_0")];
tensor<fp16, [1, ?]> var_613_cast_fp16 = reshape(shape = var_612, x = var_607_cast_fp16)[name = string("op_613_cast_fp16")];
string var_613_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_613_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
tensor<fp32, [1, ?]> wav = cast(dtype = var_613_cast_fp16_to_fp32_dtype_0, x = var_613_cast_fp16)[name = string("cast_5")];
} -> (wav);
} |