code: def setUp(self):
      self.policy_network = LinearRegressionUCB(2)
      self.eval_module = PolicyEvaluator(self.policy_network)
      self.batch = CBInput(
          context_arm_features=torch.tensor(
              [
                  [
                      [1, 2],
                      [1, 3],
                  ],
                  [
                      [1, 4],
                      [1, 5],
                  ],
              ],
              dtype=torch.float,
          ),
          action=torch.tensor([[0], [1]], dtype=torch.long),
          reward=torch.tensor([[1.5], [2.3]], dtype=torch.float),
          action_log_probability=torch.tensor(
              [[-2.0], [-3.0]], dtype=torch.float
          ),
      )
  
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 1 for calling the TestPolicyEvaluator obj's underlying member method code with example usage: obj.setUp() without return types