for (int i = 0; i < 100; ++i) {
    TensorHandlePtr multiply_result(
        Multiply(context.get(), combined_value.get(), combined_value.get(),
                 status.get()));
    ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
    std::array<TensorHandlePtr, 2> out_components;
    ExtractPerDeviceValues(context.get(), multiply_result.get(),
                           &out_components, status.get());

  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());

  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(

      });
  done.WaitForNotification();
}

TF_ShapeAndTypeList* TF_NewShapeAndTypeList(int num_items) {
  TF_ShapeAndTypeList* result = new TF_ShapeAndTypeList;
  result->num_items = num_items;
  result->items = (num_items == 0) ? nullptr : new TF_ShapeAndType[num_items]();
  return result;
}

void TF_ShapeAndTypeListSetShape(TF_ShapeAndTypeList* shape_list, int index,

  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
  ASSERT_EQ(outputs->size(), 1);
  ParallelTensor* parallel_result = (*outputs)[0].get();
  ExpectScalarEq<float>(parallel_result->tensor(0), 3.);
  ExpectScalarEq<float>(parallel_result->tensor(1), 3.);

  run_collective = parallel_device.ScalarsFromSequence<bool>(
      {true, false}, context.get(), status.get());

        "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, ::tensorflow::Env::Default()->DeleteFile(filename));
}

bool TF_StringStreamNext(TF_StringStream* list, const char** result) {
  if (list->position >= list->list->size()) {
    *result = nullptr;
    return false;
  }

  *result = list->list->at(list->position++).c_str();
  return true;
}

void TF_StringStreamDone(TF_StringStream* list) {
  delete list->list;
  delete list;

  ASSERT_TF_OK(status_);

  std::string result;
  result.resize(10);
  int64_t read = tf_random_access_file::Read(file, 0, result.length(),
                                             &result[0], status_);
  ASSERT_EQ(read, 10) << "Read: " << read << "\n";
  ASSERT_TF_OK(status_);
  ASSERT_EQ(result, "0123456789") << "Result: " << result << "\n";

  read = tf_random_access_file::Read(file, result.length(), result.length(),

  result_content.reserve(parallel_results.size());
  for (std::unique_ptr<ParallelTensor>& parallel_result : parallel_results) {
    result_content.push_back(
        MaybeParallelTensorOwned(std::move(parallel_result)));
  }
  result.emplace(std::move(result_content));
  return result;
}

// Used as an argument to TFE_NewCustomDeviceTensorHandle, indicating how
// ParallelTensors wrapped in TFE_TensorHandles should be cleaned up once their

  TFE_TensorHandle* raw_result_handle;
  int num_retvals = 1;
  TFE_Execute(op.get(), &raw_result_handle, &num_retvals, status.get());
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
  TensorHandlePtr reduced(raw_result_handle);

  std::array<TensorHandlePtr, 2> result_components;
  ExtractPerDeviceValues(context.get(), reduced.get(), &result_components,
                         status.get());