%tl = "tf.TensorListFromTensor"(%arg0, %elem_shape) : (tensor<10x8x9xf32>, tensor<2xi32>) -> tensor<!tf_type.variant<tensor<8x9xf32>>>
  // CHECK: %[[IND_SHAPE:.*]] = "tf.Const"() <{value = dense<[5, 1]> : tensor<2xi32>}> : () -> tensor<2xi32>
  // CHECK: %[[IND_RESHPE:.*]] = "tf.Reshape"(%[[ARG1]], %[[IND_SHAPE]]) : (tensor<5xi32>, tensor<2xi32>) -> tensor<5x1xi32>

      return failure();
    }
    // It is already a 1-D constant, no change.
    auto old_shape = shape.getShapedType().getShape();
    if (old_shape.size() == 1) {
      return failure();
    }
    // Verify all the leading dimensions are length one, except the last one.
    for (auto it = ++old_shape.rbegin(); it != old_shape.rend(); ++it) {
      if (*it != 1) {
        reshape->emitError(

    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();

    Arg<TF_NumberTensor, [{1-D.  The `values` of the first `SparseTensor`, size `[nnz]` Vector.}]>:$a_values,
    Arg<TF_Int64Tensor, [{1-D.  The `shape` of the first `SparseTensor`, size `[ndims]` Vector.}]>:$a_shape,
    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,