void Compute(OpKernelContext* context) override {
    // Read the `rt_nested_splits` input & convert to Eigen tensors.
    OpInputList rt_nested_splits_in;
    OP_REQUIRES_OK(
        context, context->input_list("rt_nested_splits", &rt_nested_splits_in));
    const int rt_nested_splits_len = rt_nested_splits_in.size();
    DCHECK_GT(rt_nested_splits_len, 0);  // Enforced by REGISTER_OP.
    std::vector<ConstFlatSplits> rt_nested_splits;
    rt_nested_splits.reserve(rt_nested_splits_len);
    for (int i = 0; i < rt_nested_splits_len; ++i) {
      rt_nested_splits.push_back(rt_nested_splits_in[i].flat<SPLITS_TYPE>());
    }

    // Read the `rt_dense_values` input.
    const Tensor& rt_dense_values_in = context->input(rt_nested_splits_len);
    OP_REQUIRES_OK(context,
                   ValidateInputs(rt_nested_splits, rt_dense_values_in));

    // Assemble each value in `sparse_indices` using three parts:
    // - `index_prefix` is the index in dimensions up through the last ragged
    //   dimension.
    // - `index_middle` is the index in the last ragged dimension.
    // - `index_suffix` is the index in the dense value dimensions.
    std::vector<int64> index_prefix(rt_nested_splits_len);
    std::vector<std::vector<int64>> index_suffixes =
        MakeIndexSuffixes(rt_dense_values_in.shape());

    // Allocate the `sparse_indices` output tensor.
    const int64_t nvals =
        (rt_nested_splits.back()(rt_nested_splits.back().size() - 1) *
         index_suffixes.size());
    const int64_t indices_len =
        rt_nested_splits_len + rt_dense_values_in.dims();
    Tensor* sparse_indices_out = nullptr;
    OP_REQUIRES_OK(
        context, context->allocate_output(0, TensorShape({nvals, indices_len}),
                                          &sparse_indices_out));
    auto sparse_indices = sparse_indices_out->tensor<int64, 2>();

    // pos[i] is the current position in rt_nested_splits[i].  final_pos is a
    // reference to make it easier to refer to pos[-1].
    std::vector<int64> pos(rt_nested_splits_len);
    int64& final_pos = pos[rt_nested_splits_len - 1];

    // Each iteration through the loop, we increment pos[-1], and add indices
    // for all the values corresponding to
    // rt_nested_splits[-1][pos[-1]:pos[-1]+1].
    int next_index = 0;
    int max_final_pos = rt_nested_splits.back().size() - 1;
    for (; final_pos < max_final_pos; ++final_pos) {
      // Update `pos` to skip over completed elements (i.e., elements where
      // we have already generated indices for all contained values).
      for (int dim = rt_nested_splits_len - 2; dim >= 0; --dim) {
        while (IsCompleted(pos, dim, rt_nested_splits)) {
          pos[dim] += 1;
        }
      }

      // Update index_prefix.
      for (int dim = 0; dim < index_prefix.size(); ++dim) {
        int start = dim > 0 ? rt_nested_splits[dim - 1](pos[dim - 1]) : 0;
        index_prefix[dim] = pos[dim] - start;
      }

      // Get length of the final-ragged-dimension slice.
      const auto& final_splits = rt_nested_splits[rt_nested_splits_len - 1];
      int64_t slice_len = final_splits(final_pos + 1) - final_splits(final_pos);

      // Add sparse_indices for this slice.
      for (int64_t i = 0; i < slice_len; ++i) {
        for (const auto& index_suffix : index_suffixes) {
          int dim = 0;
          for (int64_t index : index_prefix) {  // index_prefix
            sparse_indices(next_index, dim++) = index;
          }
          sparse_indices(next_index, dim++) = i;  // index_middle
          for (int64_t index : index_suffix) {    // index_suffix
            sparse_indices(next_index, dim++) = index;
          }
          DCHECK_EQ(dim, indices_len);
          ++next_index;
        }
      }
    }
    DCHECK_EQ(next_index, nvals);

    // Output the `sparse_values` Tensor.
    if (rt_dense_values_in.dims() == 1) {
      context->set_output(1, rt_dense_values_in);
    } else {
      Tensor sparse_values_out(rt_dense_values_in.dtype());
      bool shapes_match = sparse_values_out.CopyFrom(
          rt_dense_values_in, {rt_dense_values_in.NumElements()});
      DCHECK(shapes_match);
      context->set_output(1, sparse_values_out);
    }

    // Output the `sparse_dense_shape` Tensor.
    int64_t ndims = rt_nested_splits_len + rt_dense_values_in.dims();
    Tensor* sparse_dense_shape_out = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output(2, TensorShape({ndims}),
                                                     &sparse_dense_shape_out));
    auto sparse_dense_shape = sparse_dense_shape_out->vec<int64>();
    sparse_dense_shape(0) = rt_nested_splits_in[0].dim_size(0) - 1;
    for (int dim = 0; dim < rt_nested_splits_len; ++dim) {
      const auto& splits = rt_nested_splits[dim];
      SPLITS_TYPE max_width = 0;
      for (int i = 1; i < splits.size(); ++i) {
        max_width = std::max(max_width, splits(i) - splits(i - 1));
      }
      sparse_dense_shape(dim + 1) = max_width;
    }
    for (int dim = 1; dim < rt_dense_values_in.dims(); ++dim) {
      sparse_dense_shape(dim + rt_nested_splits_len) =
          rt_dense_values_in.dim_size(dim);
    }
  }