code: def test_logger(self):
      logger = TensorBoardLogger("/tmp/tb")
      logger.log_metrics = MagicMock()
      self.eval_module.attach_logger(logger)
      self.eval_module.log_metrics(step=5)
  
      expected_metric_dict = {
          "[model]Offline_Eval_avg_reward": 0.0,
          "[model]Offline_Eval_sum_weight_accepted": 0.0,
          "[model]Offline_Eval_sum_weight_all_data": 0.0,
          "[model]Offline_Eval_num_eval_model_updates": 0,
          "[model]Offline_Eval_frac_accepted": 0.0,
          "[model]Offline_Eval_avg_reward_accepted": 0.0,
          "[model]Offline_Eval_avg_reward_rejected": 0.0,
          "[model]Offline_Eval_avg_size_accepted": 0.0,
          "[model]Offline_Eval_avg_size_rejected": 0.0,
          "[model]Offline_Eval_accepted_rejected_reward_ratio": 0.0,
          "[model]Offline_Eval_avg_reward_all_data": 0.0,
      }
      logger.log_metrics.assert_called_once_with(
          expected_metric_dict, step=5
      )
  
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 7 for calling the TestPolicyEvaluator obj's underlying member method code with example usage: obj.test_logger() without return types