Type output_type = op.getResult().getType();
    const int64_t rank = operand_type.getRank();

    ArrayRef<int64_t> idx_shape(rank);
    TensorType idx_type =
        operand_type.cloneWith(idx_shape, rewriter.getI32Type());

    ArrayRef<int64_t> start_idx_i64 = op.getStartIndices();
    ArrayRef<int64_t> limit_idx_i64 = op.getLimitIndices();

  typedef VariantSubTypeBuilder Builder;
  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
    VT_SHAPE = 4,
    VT_TYPE = 6,
    VT_HAS_RANK = 8
  };
  const ::flatbuffers::Vector<int32_t> *shape() const {
    return GetPointer<const ::flatbuffers::Vector<int32_t> *>(VT_SHAPE);
  }
  tflite::TensorType type() const {
    return static_cast<tflite::TensorType>(GetField<int8_t>(VT_TYPE, 0));

  // CHECK-DAG: [[RNG_SHAPE:%.*]] = mhlo.constant dense<4> : tensor<1xi64>
  // CHECK-DAG: [[RNG_LOWER:%.*]] = mhlo.constant dense<0> : tensor<i32>
  // CHECK-DAG: [[RNG_UPPER:%.*]] = mhlo.constant dense<4> : tensor<i32>
  // CHECK: [[SWAPS:%.*]] = "mhlo.rng"([[RNG_LOWER]], [[RNG_UPPER]], [[RNG_SHAPE]]) <{rng_distribution = #mhlo.rng_distribution<UNIFORM>}>

    Arg<TF_Int64Tensor, [{2-D.  The `indices` of the second `SparseTensor`, size `[nnz, ndims]` Matrix.}]>:$b_indices,
    Arg<TF_NumberTensor, [{1-D.  The `values` of the second `SparseTensor`, size `[nnz]` Vector.}]>:$b_values,
    Arg<TF_Int64Tensor, [{1-D.  The `shape` of the second `SparseTensor`, size `[ndims]` Vector.}]>:$b_shape,