*tensor =
      unwrap(TF_CreateAbstractTensorFromEagerTensor(input_eager, status.get()));
  return absl::OkStatus();
}

// Places data from `t` into *result_tensor.
Status GetValue(AbstractTensorHandle* t, TF_Tensor** result_tensor);
}  // namespace tensorflow

    ++task_iter;
  }
  auto results = coord_agent->GetTaskState(task_vec);
  if (!results.ok()) {
    status->status = results.status();
    return;
  }
  auto* state_iter = static_cast<TF_Status*>(states);
  for (size_t i = 0; i < tasks.length; ++i) {
    const auto& result = (*results)[i];
    TF_Status s;
    TF_SetStatus(&s, static_cast<TF_Code>(result.error_code()),

        "NotDifferentiableGradientFunction.");
  }
  return gradient_function->Compute(ctx_, output_gradients, result);
}

Status TapeVSpace::BuildOnesLike(const TapeTensor& t,
                                 AbstractTensorHandle** result) const {
  AbstractOperationPtr op(ctx_->CreateOperation());
  TF_RETURN_IF_ERROR(op->Reset("OnesLike", /*raw_device_name=*/nullptr));
  if (isa<tracing::TracingOperation>(op.get())) {

}

Status GetValue(AbstractTensorHandle* t, TF_Tensor** result_tensor) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  TFE_TensorHandle* result_t =
      TF_AbstractTensorGetEagerTensor(wrap(t), status.get());
  TF_RETURN_IF_ERROR(StatusFromTF_Status(status.get()));
  *result_tensor = TFE_TensorHandleResolve(result_t, status.get());
  return StatusFromTF_Status(status.get());
}

      ImmediateExecutionTensorHandle** result) override {
    handle->Ref();
    TF_Status status;
    TFE_TensorHandle* result_handle = device_.copy_tensor_to_device(
        context_, tensorflow::wrap(handle), &status, info_);
    handle->Unref();
    if (!status.status.ok()) return status.status;
    *result = tensorflow::unwrap(result_handle);
    (*result)->Ref();
    TFE_DeleteTensorHandle(result_handle);

  DCHECK(tf_results->return_nodes.empty());
  tf_results->return_nodes.resize(results.return_nodes.size());
  for (int i = 0; i < results.return_nodes.size(); ++i) {
    tf_results->return_nodes[i] = ToOperation(results.return_nodes[i]);
  }

  // Populate missing unused map keys
  DCHECK(tf_results->missing_unused_key_names.empty());
  DCHECK(tf_results->missing_unused_key_indexes.empty());

#   TFCI is a comma-separated list of filenames from the envs directory, which
#   are all settings for the scripts. TF's CI jobs are all made of a combination
#   of these env files.
#
#   If you've clicked on a test result from our CI (via a dashboard or GitHub link),
#   click to "Invocation Details" and find BUILD_CONFIG, which will contain a TFCI
#   value in the "env_vars" list that you can choose to copy that environment.

  ASSERT_EQ(errors::OK, s.code()) << s.message();

  TF_Tensor* result_tensor;
  s = GetValue(outputs[0], &result_tensor);
  ASSERT_EQ(errors::OK, s.code()) << s.message();
  auto result_value = static_cast<float*>(TF_TensorData(result_tensor));
  EXPECT_EQ(*result_value, 1.0);
  outputs[0]->Unref();
  TF_DeleteTensor(result_tensor);
  result_tensor = nullptr;
}

INSTANTIATE_TEST_SUITE_P(

        result_op_it != result.op_tape.end()) {
      continue;
    }
    CHECK(result.op_tape.emplace(op_id, op_it->second).second);
    for (auto it : op_it->second.input_tensor_id) {
      auto count_it = result.tensor_usage_counts.find(it);
      if (count_it != result.tensor_usage_counts.end()) {
        count_it->second++;
      } else {
        result.tensor_usage_counts[it] = 1;

  TF_Operation** return_opers;
  int num_return_opers;
  TF_ImportGraphDefResultsReturnOperations(results, &num_return_opers,
                                           &return_opers);
  ASSERT_EQ(1, num_return_opers);
  EXPECT_EQ(scalar2, return_opers[0]);  // not remapped

  TF_DeleteImportGraphDefResults(results);

  // Import again, with control dependencies, into the same graph.
  TF_DeleteImportGraphDefOptions(opts);