code: def test_evaluate_calculates_average_loss(self):
      losses = [7.0, 9.0, 8.0]
      outputs = [{"loss": torch.Tensor([loss])} for loss in losses]
      data_loader = DummyDataLoader(outputs)
      metrics = self.evaluator(DummyModel(), data_loader, "")  # type: ignore
      assert metrics["loss"] == pytest.approx(8.0)
  
imports: from typing import Iterator, List, Dict
  import torch
  import pytest
  from allennlp.common.testing import AllenNlpTestCase
  from allennlp.data.data_loaders import TensorDict
  from allennlp.models import Model
  from allennlp.evaluation import Evaluator
  from allennlp.common import Params
label_desc: Use image node_id 2 for calling the TestEvaluator obj's underlying member method code with example usage: obj.test_evaluate_calculates_average_loss() without return types