  void Compute(OpKernelContext* context) override {
    const Tensor& input = context->input(0);
    const Tensor& indices = context->input(1);
    const Tensor& segment_ids = context->input(2);

    Index output_rows = -1;
    if (has_num_segments_) {
      const Tensor& num_segments = context->input(3);

      OP_REQUIRES(
          context, num_segments.shape().dims() == 0,
          errors::InvalidArgument("num_segments should be a scalar, not shape ",
                                  num_segments.shape().DebugString()));
      output_rows = internal::SubtleMustCopy(num_segments.scalar<int32>()());
      OP_REQUIRES(context, output_rows >= 0,
                  errors::InvalidArgument("segment ids must be >= 0"));
    }

    OP_REQUIRES(context, TensorShapeUtils::IsVector(indices.shape()),
                errors::InvalidArgument("indices should be a vector."));
    OP_REQUIRES(context, TensorShapeUtils::IsVector(segment_ids.shape()),
                errors::InvalidArgument("segment_ids should be a vector."));

    const int64 num_indices = indices.NumElements();
    OP_REQUIRES(context, num_indices == segment_ids.NumElements(),
                errors::InvalidArgument(
                    "segment_ids and indices should have same size."));

    auto input_flat = input.flat_outer_dims<T>();
    const int64 num_col = input_flat.dimension(1);
    const auto indices_vec = indices.vec<Index>();
    typedef int32 OutputRow;
    const auto segment_vec = segment_ids.vec<OutputRow>();
    // Note that the current implementation assumes that segment_vec values are
    // sorted.
    const OutputRow last_segment_id_plus_one =
        num_indices > 0
            ? internal::SubtleMustCopy(segment_vec(num_indices - 1)) + 1
            : 0;
    if (has_num_segments_) {
      OP_REQUIRES(
          context, output_rows >= last_segment_id_plus_one,
          errors::InvalidArgument("segment ids must be < num_segments"));
    } else {
      output_rows = last_segment_id_plus_one;
    }
    OP_REQUIRES(context, output_rows >= 0,
                errors::InvalidArgument("segment ids must be >= 0"));

    TensorShape output_shape = input.shape();
    output_shape.set_dim(0, output_rows);

    // Note that we do not initialize the output buffer with a default value, so
    // we need to explicitly set missing indices to the default value.
    Tensor* output = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));
    if (num_indices == 0) {
      if (output_rows > 0) {
        output->flat_outer_dims<T>().setConstant(default_value_);
      }
      return;
    }
    OP_REQUIRES(context, output_rows > 0,
                errors::InvalidArgument("segment ids must be >= 0"));
    auto output_flat = output->flat_outer_dims<T>();

    int64 start = 0, end = 1;
    // Index from which the output is not initialized.
    OutputRow uninitialized_index = 0;
    OutputRow out_index = internal::SubtleMustCopy(segment_vec(start));

    while (true) {
      // We initialize next_index to 0 to avoid "warning: 'next_index' may be
      // used uninitialized in this function" in the Mac build (since the
      // compiler isn't smart enough to realize the code is safe).
      OutputRow next_index = 0;
      if (end < num_indices) {
        next_index = internal::SubtleMustCopy(segment_vec(end));
        if (out_index == next_index) {
          ++end;
          continue;
        }
        // We have a new segment here.  Verify that the segment ids are growing.
        OP_REQUIRES(context, out_index < next_index,
                    errors::InvalidArgument("segment ids are not increasing"));
      }

      OP_REQUIRES(
          context, FastBoundsCheck(out_index, output_rows),
          errors::InvalidArgument(
              "Segment id ", out_index, " out of range [0, ", output_rows,
              "), possibly because 'segment_ids' input is not sorted."));

      // If there is a gap between two indices, we need to set that gap to the
      // default value.
      if (out_index > uninitialized_index) {
        Eigen::DSizes<Eigen::DenseIndex, 2> gap_slice_shape(
            out_index - uninitialized_index, num_col);
        Eigen::TensorMap<Eigen::Tensor<T, 2, Eigen::RowMajor>, Eigen::Unaligned>
            gap_slice(&output_flat(uninitialized_index, 0), gap_slice_shape);
        gap_slice.setConstant(default_value_);
      }

      auto out = output_flat.template chip<0>(out_index);
      const int bad_offset =
          Reduce(input_flat, indices_vec, start, end - start, out);
      OP_REQUIRES(context, bad_offset < 0,
                  errors::InvalidArgument(
                      "Bad: indices[", start + bad_offset,
                      "] == ", indices_vec(start + bad_offset),
                      " out of range [0, ", input_flat.dimension(0), ")"));

      start = end;
      ++end;
      uninitialized_index = out_index + 1;
      out_index = next_index;
      if (end > num_indices) break;
    }

    // Fill the gap at the end with the default value.
    if (uninitialized_index < output_rows) {
      Eigen::DSizes<Eigen::DenseIndex, 2> gap_slice_shape(
          output_rows - uninitialized_index, num_col);
      Eigen::TensorMap<Eigen::Tensor<T, 2, Eigen::RowMajor>, Eigen::Unaligned>
          gap_slice(&output_flat(uninitialized_index, 0), gap_slice_shape);
      gap_slice.setConstant(default_value_);
    }
  }
  void Compute(OpKernelContext* context) override {
    const Tensor& input = context->input(0);
    const Tensor& indices = context->input(1);
    const Tensor& segment_ids = context->input(2);

    Index output_rows = -1;
    if (has_num_segments_) {
      const Tensor& num_segments = context->input(3);

      OP_REQUIRES(
          context, num_segments.shape().dims() == 0,
          errors::InvalidArgument("num_segments should be a scalar, not shape ",
                                  num_segments.shape().DebugString()));
      output_rows = internal::SubtleMustCopy(num_segments.scalar<int32>()());
      OP_REQUIRES(context, output_rows >= 0,
                  errors::InvalidArgument("segment ids must be >= 0"));
    }

    OP_REQUIRES(context, TensorShapeUtils::IsVector(indices.shape()),
                errors::InvalidArgument("indices should be a vector."));
    OP_REQUIRES(context, TensorShapeUtils::IsVector(segment_ids.shape()),
                errors::InvalidArgument("segment_ids should be a vector."));

    const int64 num_indices = indices.NumElements();
    OP_REQUIRES(context, num_indices == segment_ids.NumElements(),
                errors::InvalidArgument(
                    "segment_ids and indices should have same size."));

    auto input_flat = input.flat_outer_dims<T>();
    const int64 num_col = input_flat.dimension(1);
    const auto indices_vec = indices.vec<Index>();
    typedef int32 OutputRow;
    const auto segment_vec = segment_ids.vec<OutputRow>();
     
     
    const OutputRow last_segment_id_plus_one =
        num_indices > 0
            ? internal::SubtleMustCopy(segment_vec(num_indices - 1)) + 1
            : 0;
    if (has_num_segments_) {
      OP_REQUIRES(
          context, output_rows >= last_segment_id_plus_one,
          errors::InvalidArgument("segment ids must be < num_segments"));
    } else {
      output_rows = last_segment_id_plus_one;
    }
    OP_REQUIRES(context, output_rows >= 0,
                errors::InvalidArgument("segment ids must be >= 0"));

    TensorShape output_shape = input.shape();
    output_shape.set_dim(0, output_rows);

     
     
    Tensor* output = nullptr;
    OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));
    if (num_indices == 0) {
      if (output_rows > 0) {
        output->flat_outer_dims<T>().setConstant(default_value_);
      }
      return;
    }
    OP_REQUIRES(context, output_rows > 0,
                errors::InvalidArgument("segment ids must be >= 0"));
    auto output_flat = output->flat_outer_dims<T>();

    int64 start = 0, end = 1;
     
    OutputRow uninitialized_index = 0;
    OutputRow out_index = internal::SubtleMustCopy(segment_vec(start));

    while (true) {
       
       
       
      OutputRow next_index = 0;
      if (end < num_indices) {
        next_index = internal::SubtleMustCopy(segment_vec(end));
        if (out_index == next_index) {
          ++end;
          continue;
        }
         
        OP_REQUIRES(context, out_index < next_index,
                    errors::InvalidArgument("segment ids are not increasing"));
      }

      OP_REQUIRES(
          context, FastBoundsCheck(out_index, output_rows),
          errors::InvalidArgument(
              "Segment id ", out_index, " out of range [0, ", output_rows,
              "), possibly because 'segment_ids' input is not sorted."));

       
       
      if (out_index > uninitialized_index) {
        Eigen::DSizes<Eigen::DenseIndex, 2> gap_slice_shape(
            out_index - uninitialized_index, num_col);
        Eigen::TensorMap<Eigen::Tensor<T, 2, Eigen::RowMajor>, Eigen::Unaligned>
            gap_slice(&output_flat(uninitialized_index, 0), gap_slice_shape);
        gap_slice.setConstant(default_value_);
      }

      auto out = output_flat.template chip<0>(out_index);
      const int bad_offset =
          Reduce(input_flat, indices_vec, start, end - start, out);
      OP_REQUIRES(context, bad_offset < 0,
                  errors::InvalidArgument(
                      "Bad: indices[", start + bad_offset,
                      "] == ", indices_vec(start + bad_offset),
                      " out of range [0, ", input_flat.dimension(0), ")"));

      start = end;
      ++end;
      uninitialized_index = out_index + 1;
      out_index = next_index;
      if (end > num_indices) break;
    }

     
    if (uninitialized_index < output_rows) {
      Eigen::DSizes<Eigen::DenseIndex, 2> gap_slice_shape(
          output_rows - uninitialized_index, num_col);
      Eigen::TensorMap<Eigen::Tensor<T, 2, Eigen::RowMajor>, Eigen::Unaligned>
          gap_slice(&output_flat(uninitialized_index, 0), gap_slice_shape);
      gap_slice.setConstant(default_value_);
    }
  }