code: def test_linucb_training_multiple_epochs(self):
      # make sure that splitting the data across multiple epochs is same as learning from all data in one epoch
      # this is only true when there is no discounting (gamma=1)
      obss = []
      for i in range(self.batch_size):
          obss.append(
              CBInput(
                  context_arm_features=self.batch.context_arm_features[
                      i : i + 1, :, :
                  ],
                  action=self.batch.action[[i]],
                  reward=self.batch.reward[[i]],
              )
          )
  
      scorer_1 = LinearRegressionUCB(self.x_dim)
      scorer_2 = LinearRegressionUCB(self.x_dim)
      policy_1 = Policy(scorer=scorer_1, sampler=GreedyActionSampler())
      policy_2 = Policy(scorer=scorer_2, sampler=GreedyActionSampler())
      trainer_1 = LinUCBTrainer(policy_1)
      trainer_2 = LinUCBTrainer(policy_2)
  
      trainer_1.training_step(obss[0], 0)
      trainer_1.on_train_epoch_end()
      trainer_1.training_step(obss[1], 1)
      trainer_1.on_train_epoch_end()
  
      trainer_2.training_step(self.batch, 0)
      trainer_2.on_train_epoch_end()
  
      npt.assert_array_less(
          np.zeros(scorer_1.avg_A.shape), scorer_1.avg_A.numpy()
      )  # make sure A got updated
      npt.assert_allclose(
          scorer_1.avg_A.numpy(), scorer_2.avg_A.numpy(), rtol=1e-4
      )
      npt.assert_allclose(
          scorer_1.avg_b.numpy(), scorer_2.avg_b.numpy(), rtol=1e-4
      )
      npt.assert_allclose(
          scorer_1.inv_avg_A.numpy(),
          scorer_2.inv_avg_A.numpy(),
          rtol=1e-4,
      )
      npt.assert_allclose(
          scorer_1.coefs.numpy(), scorer_2.coefs.numpy(), rtol=1e-3
      )
  
imports: import copy
  import unittest
  import numpy
  import numpy.testing
  import torch
  from libfb.py.collections import AttrDict
  from pytorch_lightning.loggers import TensorBoardLogger
  from reagent.core.types import CBInput
  from reagent.gym.policies.policy import Policy
  from reagent.gym.policies.samplers.discrete_sampler import GreedyActionSampler
  from reagent.models.linear_regression import LinearRegressionUCB
  from reagent.training.cb.linucb_trainer import LinUCBTrainer
  from reagent.training.cb.utils import add_chosen_arm_features
  from reagent.training.parameters import LinUCBTrainerParameters
  from torchrec.fb.metrics.meta_config import DefaultTaskInfo, MetricsConfig, QPSDef, RecMetricDef, RecMetricEnum
  from torchrec.fb.metrics.meta_metrics import generate_metric_module, RecMetricModule
label_desc: Use image node_id 4 for calling the TestLinUCB obj's underlying member method code with example usage: obj.test_linucb_training_multiple_epochs() without return types