code: def test_ingest_batch(self):
      model_actions = torch.tensor([[1], [1]], dtype=torch.long)
      _ = self.eval_module.ingest_batch(self.batch, model_actions)
      self.eval_module._aggregate_across_instances()
      # correct average reward
      self.assertEqual(
          self.eval_module.get_avg_reward(),
          self.batch.reward[1, 0].item(),
      )
  
imports: import copy
  import unittest
  from dataclasses import replace
  from unittest.mock import MagicMock
  import torch
  from pytorch_lightning.loggers import TensorBoardLogger
  from reagent.core.types import CBInput
  from reagent.evaluation.cb.policy_evaluator import PolicyEvaluator
  from reagent.evaluation.cb.utils import add_importance_weights
  from reagent.models.linear_regression import LinearRegressionUCB
label_desc: Use image node_id 5 for calling the TestPolicyEvaluator obj's underlying member method code with example usage: obj.test_ingest_batch() without return types