code: def test_evaluate_calculates_average_loss_with_weights(self):
      losses = [7.0, 9.0, 8.0]
      weights = [10, 2, 1.5]
      inputs = zip(losses, weights)
      outputs = [
          {
              "loss": torch.Tensor([loss]),
              "batch_weight": torch.Tensor([weight]),
          }
          for loss, weight in inputs
      ]
      data_loader = DummyDataLoader(outputs)
      metrics = self.evaluator(DummyModel(), data_loader, "batch_weight")  # type: ignore
      assert metrics["loss"] == pytest.approx((70 + 18 + 12) / 13.5)
  
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 3 for calling the TestEvaluator obj's underlying member method code with example usage: obj.test_evaluate_calculates_average_loss_with_weights() without return types