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

    if (!SegmentReductionDoValidation(context, input, segment_ids)) {
      return;
    }

    const int64 num_indices = segment_ids.NumElements();
    auto input_flat = input.flat_outer_dims<T>();
    const int64 num_col = input_flat.dimension(1);

    const auto segment_vec = segment_ids.vec<Index>();
    // Note that the current implementation assumes that segment_vec values are
    // sorted.
    const Index output_rows =
        num_indices > 0
            ? internal::SubtleMustCopy(segment_vec(num_indices - 1)) + 1
            : 0;
    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) return;
    OP_REQUIRES(context, output_rows > 0,
                errors::InvalidArgument("segment ids must be >= 0"));
    auto output_flat = output->flat_outer_dims<T>();

#if !defined(EIGEN_HAS_INDEX_LIST)
    Eigen::DSizes<Eigen::DenseIndex, 1> dims_to_reduce;
    dims_to_reduce[0] = 0;
#else
    Eigen::IndexList<Eigen::type2index<0> > dims_to_reduce;
#endif
    Index start = 0, end = 1;

    Index uninitialized_index = 0;  // Index from which the output is not set.
    Index out_index = internal::SubtleMustCopy(segment_vec(start));

    // TODO(agarwal): if this loop becomes a bottleneck, consider sharding it
    // across threads.
    Eigen::DSizes<Eigen::DenseIndex, 1> out_slice_shape(num_col);
    while (end <= num_indices) {
      // 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).
      Index 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"));
      }

      // Process segment [start, end)
      const T* in_slice_ptr = &input_flat(start, 0);
      typedef Eigen::TensorMap<Eigen::Tensor<T, 1, Eigen::RowMajor>,
                               Eigen::Unaligned>
          OutT;

      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(T(default_value));
      }

      T* out_slice_ptr = &output_flat(out_index, 0);
      OutT out_slice(out_slice_ptr, out_slice_shape);
      // We don't use out_slice.device(context->eigen_device<Device>)
      // because these pieces of work are likely to be very small and
      // the context switching overhead dwarfs any benefit we get from
      // using another thread to do this work.
      if (start == end - 1) {
        typedef Eigen::TensorMap<Eigen::Tensor<const T, 1, Eigen::RowMajor>,
                                 Eigen::Unaligned>
            InT;
        InT in_slice(in_slice_ptr, out_slice_shape);
        out_slice = in_slice;
      } else {
        Eigen::DSizes<Eigen::DenseIndex, 2> in_slice_shape(end - start,
                                                           num_col);
        typedef Eigen::TensorMap<Eigen::Tensor<const T, 2, Eigen::RowMajor>,
                                 Eigen::Unaligned>
            InT;
        InT in_slice(in_slice_ptr, in_slice_shape);

        out_slice = in_slice.reduce(dims_to_reduce, Reducer());
      }
      if (end >= num_indices) break;
      start = end;
      ++end;
      uninitialized_index = out_index + 1;
      out_index = next_index;
    }
  }
  void Compute(OpKernelContext* context) override {
    const Tensor& input = context->input(0);
    const Tensor& segment_ids = context->input(1);

    if (!SegmentReductionDoValidation(context, input, segment_ids)) {
      return;
    }

    const int64 num_indices = segment_ids.NumElements();
    auto input_flat = input.flat_outer_dims<T>();
    const int64 num_col = input_flat.dimension(1);

    const auto segment_vec = segment_ids.vec<Index>();
     
     
    const Index output_rows =
        num_indices > 0
            ? internal::SubtleMustCopy(segment_vec(num_indices - 1)) + 1
            : 0;
    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) return;
    OP_REQUIRES(context, output_rows > 0,
                errors::InvalidArgument("segment ids must be >= 0"));
    auto output_flat = output->flat_outer_dims<T>();

#if !defined(EIGEN_HAS_INDEX_LIST)
    Eigen::DSizes<Eigen::DenseIndex, 1> dims_to_reduce;
    dims_to_reduce[0] = 0;
#else
    Eigen::IndexList<Eigen::type2index<0> > dims_to_reduce;
#endif
    Index start = 0, end = 1;

    Index uninitialized_index = 0;   
    Index out_index = internal::SubtleMustCopy(segment_vec(start));

     
     
    Eigen::DSizes<Eigen::DenseIndex, 1> out_slice_shape(num_col);
    while (end <= num_indices) {
       
       
       
      Index 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"));
      }

       
      const T* in_slice_ptr = &input_flat(start, 0);
      typedef Eigen::TensorMap<Eigen::Tensor<T, 1, Eigen::RowMajor>,
                               Eigen::Unaligned>
          OutT;

      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(T(default_value));
      }

      T* out_slice_ptr = &output_flat(out_index, 0);
      OutT out_slice(out_slice_ptr, out_slice_shape);
       
       
       
       
      if (start == end - 1) {
        typedef Eigen::TensorMap<Eigen::Tensor<const T, 1, Eigen::RowMajor>,
                                 Eigen::Unaligned>
            InT;
        InT in_slice(in_slice_ptr, out_slice_shape);
        out_slice = in_slice;
      } else {
        Eigen::DSizes<Eigen::DenseIndex, 2> in_slice_shape(end - start,
                                                           num_col);
        typedef Eigen::TensorMap<Eigen::Tensor<const T, 2, Eigen::RowMajor>,
                                 Eigen::Unaligned>
            InT;
        InT in_slice(in_slice_ptr, in_slice_shape);

        out_slice = in_slice.reduce(dims_to_reduce, Reducer());
      }
      if (end >= num_indices) break;
      start = end;
      ++end;
      uninitialized_index = out_index + 1;
      out_index = next_index;
    }
  }