std::vector<int64_t> source_tensor_ids = MakeTensorIDList(sources);
  tensorflow::gtl::FlatSet<int64_t> sources_set(source_tensor_ids.begin(),
                                                source_tensor_ids.end());
  std::unordered_map<int64_t, TapeTensor> sources_that_are_targets;
  for (int i = 0; i < target_tensor_ids.size(); ++i) {
    int64_t target_id = target_tensor_ids[i];

  }
  if (result.size() != source_tensor_ids.size()) {
    return errors::Internal("Expected result Span to be of size ",
                            source_tensor_ids.size(), " found ", result.size(),
                            " in call to Tape::ComputeGradient.");
  }
  std::unordered_set<int64_t> used_gradient_ids(source_tensor_ids.size());
  for (int i = 0; i < source_tensor_ids.size(); i++) {

  const int last_node_id = graph->graph.num_node_ids();
  tensorflow::ImportGraphDefResults results;
  status->status = tensorflow::ImportGraphDef(opts->opts, def, &graph->graph,
                                              &graph->refiner, &results);
  if (!status->status.ok()) return;

  // Add new nodes to name_map
  for (int i = last_node_id; i < graph->graph.num_node_ids(); ++i) {
    auto* node = graph->graph.FindNodeId(i);

std::unique_ptr<ParallelTensor> ParallelDevice::DeviceIDs(
    TFE_Context* context, TF_Status* status) const {
  std::vector<int32_t> ids;
  ids.reserve(num_underlying_devices());
  for (int i = 0; i < num_underlying_devices(); ++i) {
    ids.push_back(i);
  }
  return ScalarsFromSequence<int32_t>(ids, context, status);
}

absl::optional<std::vector<std::unique_ptr<ParallelTensor>>>
ParallelDevice::Execute(TFE_Context* context,

* `tf.tpu.experimental.embedding.TPUEmbeddingV2`
    * Add `compute_sparse_core_stats` for sparse core users to profile the 
      data with this API to get the `max_ids` and `max_unique_ids`. These
      numbers will be needed to configure the sparse core embedding mid level 
      api.
    * Remove the `preprocess_features` method since that's no longer needed.

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