program(1.3)
[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}})]
{
    func main<ios18>(tensor<int32, [1, 1]> token_id) {
            int32 var_6_batch_dims_0 = const()[name = string("op_6_batch_dims_0"), val = int32(0)];
            bool var_6_validate_indices_0 = const()[name = string("op_6_validate_indices_0"), val = bool(false)];
            tensor<fp16, [151936, 1024]> embedding_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [151936, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155582592))))[name = string("embedding_weight_to_fp16_palettized")];
            int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)];
            tensor<bool, [1, 1]> greater_equal_0 = greater_equal(x = token_id, 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(151936)];
            tensor<int32, [1, 1]> add_0 = add(x = token_id, y = slice_by_index_0)[name = string("add_0")];
            tensor<int32, [1, 1]> select_0 = select(a = token_id, b = add_0, cond = greater_equal_0)[name = string("select_0")];
            int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)];
            tensor<bool, [1, 1]> greater_equal_0_1 = greater_equal(x = select_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")];
            int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(151936)];
            tensor<int32, [1, 1]> add_0_1 = add(x = select_0, y = slice_by_index_0_1)[name = string("add_0_1")];
            tensor<int32, [1, 1]> select_0_1 = select(a = select_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")];
            int32 op_6_cast_fp16_axis_0 = const()[name = string("op_6_cast_fp16_axis_0"), val = int32(0)];
            tensor<fp16, [1, 1, 1024]> embedding = gather(axis = op_6_cast_fp16_axis_0, batch_dims = var_6_batch_dims_0, indices = select_0_1, validate_indices = var_6_validate_indices_0, x = embedding_weight_to_fp16_palettized)[name = string("op_6_cast_fp16")];
        } -> (embedding);
}