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

      });
  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());

  }

  TFE_Execute(op_.get(), unwrapped_results.data(), &real_num_outputs, status);
  if (TF_GetCode(status) != TF_OK) {
    cancellation_manager_->StartCancel();
    return;
  }
  unwrapped_results.resize(real_num_outputs);
  outputs->reserve(real_num_outputs);
  for (TFE_TensorHandle* unwrapped_result : unwrapped_results) {
    outputs->emplace_back(unwrapped_result);
  }
}

      gtl::ArraySlice<AbstractTensorHandle*> output_gradients,
      absl::Span<AbstractTensorHandle*> result) const override;

  // Builds a tensor filled with ones with the same shape and dtype as `t`.
  Status BuildOnesLike(const TapeTensor& t,
                       AbstractTensorHandle** result) const override;

  // Looks up the ID of a Gradient.
  int64_t TensorId(AbstractTensorHandle* tensor) const override;

    ++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()),

  float result_data[4] = {0};
  memcpy(&result_data[0], TF_TensorData(result_tensor),
         TF_TensorByteSize(result_tensor));

  int data_len = 4;  // length of result_data
  for (int i = 0; i < data_len; i++) {
    EXPECT_EQ(result_data[i], 0);
  }

  TF_DeleteTensor(result_tensor);
  TF_DeleteAbstractTensor(result);
  TF_DeleteOutputList(o);
  TF_DeleteExecutionContext(ctx);
}

// MatMul Test 2

           bucket, gcs::Prefix(prefix), gcs::Delimiter(delimiter),
           gcs::Fields("items(name),prefixes"))) {
    if (count == max_results) {
      TF_SetStatus(status, TF_OK, "");
      return result;
    }
    if (!item) {
      TF_SetStatusFromGCSStatus(item.status(), status);
      return result;
    }
    auto value = *std::move(item);
    std::string children = std::holds_alternative<std::string>(value)

      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);

  }
}
job_name: "worker"
task_index: 1
protocol: "grpc"
)");

  TF_Status* status = TF_NewStatus();
  TF_Buffer* result = TFE_GetServerDef(expected_text_proto.c_str(), status);
  EXPECT_EQ(TF_GetCode(status), TF_OK);

  ServerDef actual;
  ASSERT_TRUE(actual.ParseFromArray(result->data, result->length));
  string actual_text_proto;
  tensorflow::protobuf::TextFormat::PrintToString(actual, &actual_text_proto);