program(1.0)
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3304.5.2"}, {"coremlc-version", "3304.6.2"}, {"coremltools-component-torch", "2.1.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.0b1"}})]
{
    func main<ios16>(tensor<fp16, [1, 3072, 8, 8]> x) {
            tensor<bool, []> var_6 = const()[name = tensor<string, []>("op_6"), val = tensor<bool, []>(true)];
            tensor<int32, []> var_9 = const()[name = tensor<string, []>("op_9"), val = tensor<int32, []>(1)];
            tensor<bool, []> x_eps_interleave_0 = const()[name = tensor<string, []>("x_eps_interleave_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [1, 1, 8, 8]> eps_chan_to_fp16 = const()[name = tensor<string, []>("eps_chan_to_fp16"), val = tensor<fp16, [1, 1, 8, 8]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
            tensor<fp16, [1, 3073, 8, 8]> x_eps_cast_fp16 = concat(axis = var_9, interleave = x_eps_interleave_0, values = (x, eps_chan_to_fp16))[name = tensor<string, []>("x_eps_cast_fp16")];
            tensor<int32, [1]> norm_x_axes_0 = const()[name = tensor<string, []>("norm_x_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 8, 8]> norm_x_cast_fp16 = reduce_l2_norm(axes = norm_x_axes_0, keep_dims = var_6, x = x_eps_cast_fp16)[name = tensor<string, []>("norm_x_cast_fp16")];
            tensor<fp16, [1, 3072, 8, 8]> x_normed_1_cast_fp16 = real_div(x = x, y = norm_x_cast_fp16)[name = tensor<string, []>("x_normed_1_cast_fp16")];
            tensor<fp16, []> var_34_to_fp16 = const()[name = tensor<string, []>("op_34_to_fp16"), val = tensor<fp16, []>(0x1.bb8p+5)];
            tensor<fp16, [1, 3072, 8, 8]> x_normed_3_cast_fp16 = mul(x = x_normed_1_cast_fp16, y = var_34_to_fp16)[name = tensor<string, []>("x_normed_3_cast_fp16")];
            tensor<fp16, [1, 3072, 1, 1]> ln_f_weight_to_fp16 = const()[name = tensor<string, []>("ln_f_weight_to_fp16"), val = tensor<fp16, [1, 3072, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(256)))];
            tensor<fp16, [1, 3072, 8, 8]> x_5_cast_fp16 = mul(x = x_normed_3_cast_fp16, y = ln_f_weight_to_fp16)[name = tensor<string, []>("x_5_cast_fp16")];
            tensor<int32, [4]> var_48 = const()[name = tensor<string, []>("op_48"), val = tensor<int32, [4]>([1, 3072, 1, -1])];
            tensor<fp16, [1, 3072, 1, 64]> x_cast_fp16 = reshape(shape = var_48, x = x_5_cast_fp16)[name = tensor<string, []>("x_cast_fp16")];
            tensor<int32, [1]> var_51_axes_0 = const()[name = tensor<string, []>("op_51_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<fp16, [1, 3072, 64]> var_51_cast_fp16 = squeeze(axes = var_51_axes_0, x = x_cast_fp16)[name = tensor<string, []>("op_51_cast_fp16")];
            tensor<int32, [3]> var_54_perm_0 = const()[name = tensor<string, []>("op_54_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
            tensor<int32, [2]> concat_4 = const()[name = tensor<string, []>("concat_4"), val = tensor<int32, [2]>([64, 3072])];
            tensor<fp16, [1, 64, 3072]> var_54_cast_fp16 = transpose(perm = var_54_perm_0, x = var_51_cast_fp16)[name = tensor<string, []>("transpose_16")];
            tensor<fp16, [64, 3072]> reshape_0_cast_fp16 = reshape(shape = concat_4, x = var_54_cast_fp16)[name = tensor<string, []>("reshape_0_cast_fp16")];
            tensor<bool, []> matmul_0_transpose_x_0 = const()[name = tensor<string, []>("matmul_0_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_0_transpose_y_0 = const()[name = tensor<string, []>("matmul_0_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [3072, 16384]> transpose_1_to_fp16 = const()[name = tensor<string, []>("transpose_1_to_fp16"), val = tensor<fp16, [3072, 16384]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6464)))];
            tensor<fp16, [64, 16384]> matmul_0_cast_fp16 = matmul(transpose_x = matmul_0_transpose_x_0, transpose_y = matmul_0_transpose_y_0, x = reshape_0_cast_fp16, y = transpose_1_to_fp16)[name = tensor<string, []>("matmul_0_cast_fp16")];
            tensor<int32, [3]> concat_8 = const()[name = tensor<string, []>("concat_8"), val = tensor<int32, [3]>([1, 64, 16384])];
            tensor<fp16, [1, 64, 16384]> logits_0 = reshape(shape = concat_8, x = matmul_0_cast_fp16)[name = tensor<string, []>("reshape_2_cast_fp16")];
            tensor<bool, []> matmul_1_transpose_x_0 = const()[name = tensor<string, []>("matmul_1_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_1_transpose_y_0 = const()[name = tensor<string, []>("matmul_1_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [3072, 16384]> transpose_3_to_fp16 = const()[name = tensor<string, []>("transpose_3_to_fp16"), val = tensor<fp16, [3072, 16384]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(100669824)))];
            tensor<fp16, [64, 16384]> matmul_1_cast_fp16 = matmul(transpose_x = matmul_1_transpose_x_0, transpose_y = matmul_1_transpose_y_0, x = reshape_0_cast_fp16, y = transpose_3_to_fp16)[name = tensor<string, []>("matmul_1_cast_fp16")];
            tensor<int32, [3]> concat_16 = const()[name = tensor<string, []>("concat_16"), val = tensor<int32, [3]>([1, 64, 16384])];
            tensor<fp16, [1, 64, 16384]> logits_1 = reshape(shape = concat_16, x = matmul_1_cast_fp16)[name = tensor<string, []>("reshape_5_cast_fp16")];
            tensor<bool, []> matmul_2_transpose_x_0 = const()[name = tensor<string, []>("matmul_2_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_2_transpose_y_0 = const()[name = tensor<string, []>("matmul_2_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [3072, 16384]> transpose_5_to_fp16 = const()[name = tensor<string, []>("transpose_5_to_fp16"), val = tensor<fp16, [3072, 16384]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(201333184)))];
            tensor<fp16, [64, 16384]> matmul_2_cast_fp16 = matmul(transpose_x = matmul_2_transpose_x_0, transpose_y = matmul_2_transpose_y_0, x = reshape_0_cast_fp16, y = transpose_5_to_fp16)[name = tensor<string, []>("matmul_2_cast_fp16")];
            tensor<int32, [3]> concat_24 = const()[name = tensor<string, []>("concat_24"), val = tensor<int32, [3]>([1, 64, 16384])];
            tensor<fp16, [1, 64, 16384]> logits_2 = reshape(shape = concat_24, x = matmul_2_cast_fp16)[name = tensor<string, []>("reshape_8_cast_fp16")];
            tensor<bool, []> matmul_3_transpose_x_0 = const()[name = tensor<string, []>("matmul_3_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_3_transpose_y_0 = const()[name = tensor<string, []>("matmul_3_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [3072, 16384]> transpose_7_to_fp16 = const()[name = tensor<string, []>("transpose_7_to_fp16"), val = tensor<fp16, [3072, 16384]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(301996544)))];
            tensor<fp16, [64, 16384]> matmul_3_cast_fp16 = matmul(transpose_x = matmul_3_transpose_x_0, transpose_y = matmul_3_transpose_y_0, x = reshape_0_cast_fp16, y = transpose_7_to_fp16)[name = tensor<string, []>("matmul_3_cast_fp16")];
            tensor<int32, [3]> concat_32 = const()[name = tensor<string, []>("concat_32"), val = tensor<int32, [3]>([1, 64, 16384])];
            tensor<fp16, [1, 64, 16384]> logits_3 = reshape(shape = concat_32, x = matmul_3_cast_fp16)[name = tensor<string, []>("reshape_11_cast_fp16")];
            tensor<bool, []> matmul_4_transpose_x_0 = const()[name = tensor<string, []>("matmul_4_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_4_transpose_y_0 = const()[name = tensor<string, []>("matmul_4_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [3072, 16384]> transpose_9_to_fp16 = const()[name = tensor<string, []>("transpose_9_to_fp16"), val = tensor<fp16, [3072, 16384]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(402659904)))];
            tensor<fp16, [64, 16384]> matmul_4_cast_fp16 = matmul(transpose_x = matmul_4_transpose_x_0, transpose_y = matmul_4_transpose_y_0, x = reshape_0_cast_fp16, y = transpose_9_to_fp16)[name = tensor<string, []>("matmul_4_cast_fp16")];
            tensor<int32, [3]> concat_40 = const()[name = tensor<string, []>("concat_40"), val = tensor<int32, [3]>([1, 64, 16384])];
            tensor<fp16, [1, 64, 16384]> logits_4 = reshape(shape = concat_40, x = matmul_4_cast_fp16)[name = tensor<string, []>("reshape_14_cast_fp16")];
            tensor<bool, []> matmul_5_transpose_x_0 = const()[name = tensor<string, []>("matmul_5_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_5_transpose_y_0 = const()[name = tensor<string, []>("matmul_5_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [3072, 16384]> transpose_11_to_fp16 = const()[name = tensor<string, []>("transpose_11_to_fp16"), val = tensor<fp16, [3072, 16384]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(503323264)))];
            tensor<fp16, [64, 16384]> matmul_5_cast_fp16 = matmul(transpose_x = matmul_5_transpose_x_0, transpose_y = matmul_5_transpose_y_0, x = reshape_0_cast_fp16, y = transpose_11_to_fp16)[name = tensor<string, []>("matmul_5_cast_fp16")];
            tensor<int32, [3]> concat_48 = const()[name = tensor<string, []>("concat_48"), val = tensor<int32, [3]>([1, 64, 16384])];
            tensor<fp16, [1, 64, 16384]> logits_5 = reshape(shape = concat_48, x = matmul_5_cast_fp16)[name = tensor<string, []>("reshape_17_cast_fp16")];
            tensor<bool, []> matmul_6_transpose_x_0 = const()[name = tensor<string, []>("matmul_6_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_6_transpose_y_0 = const()[name = tensor<string, []>("matmul_6_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [3072, 16384]> transpose_13_to_fp16 = const()[name = tensor<string, []>("transpose_13_to_fp16"), val = tensor<fp16, [3072, 16384]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(603986624)))];
            tensor<fp16, [64, 16384]> matmul_6_cast_fp16 = matmul(transpose_x = matmul_6_transpose_x_0, transpose_y = matmul_6_transpose_y_0, x = reshape_0_cast_fp16, y = transpose_13_to_fp16)[name = tensor<string, []>("matmul_6_cast_fp16")];
            tensor<int32, [3]> concat_56 = const()[name = tensor<string, []>("concat_56"), val = tensor<int32, [3]>([1, 64, 16384])];
            tensor<fp16, [1, 64, 16384]> logits_6 = reshape(shape = concat_56, x = matmul_6_cast_fp16)[name = tensor<string, []>("reshape_20_cast_fp16")];
            tensor<bool, []> matmul_7_transpose_x_0 = const()[name = tensor<string, []>("matmul_7_transpose_x_0"), val = tensor<bool, []>(false)];
            tensor<bool, []> matmul_7_transpose_y_0 = const()[name = tensor<string, []>("matmul_7_transpose_y_0"), val = tensor<bool, []>(false)];
            tensor<fp16, [3072, 13568]> transpose_15_to_fp16 = const()[name = tensor<string, []>("transpose_15_to_fp16"), val = tensor<fp16, [3072, 13568]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(704649984)))];
            tensor<fp16, [64, 13568]> matmul_7_cast_fp16 = matmul(transpose_x = matmul_7_transpose_x_0, transpose_y = matmul_7_transpose_y_0, x = reshape_0_cast_fp16, y = transpose_15_to_fp16)[name = tensor<string, []>("matmul_7_cast_fp16")];
            tensor<int32, [3]> concat_64 = const()[name = tensor<string, []>("concat_64"), val = tensor<int32, [3]>([1, 64, 13568])];
            tensor<fp16, [1, 64, 13568]> logits_7 = reshape(shape = concat_64, x = matmul_7_cast_fp16)[name = tensor<string, []>("reshape_23_cast_fp16")];
        } -> (logits_0, logits_1, logits_2, logits_3, logits_4, logits_5, logits_6, logits_7);
}