code: def setUp(self):
      self.batch_size = 2
  
      self.num_arms = 2
      self.params = LinUCBTrainerParameters()
  
      self.x_dim = 5
      policy_network = LinearRegressionUCB(self.x_dim)
      self.policy = Policy(
          scorer=policy_network, sampler=GreedyActionSampler()
      )
  
      self.trainer = LinUCBTrainer(self.policy, **self.params.asdict())
      self.batch = CBInput(
          context_arm_features=torch.tensor(
              [
                  [
                      [1, 2, 3, 6, 7],
                      [1, 2, 3, 10, 11],
                  ],
                  [
                      [1, 4, 5, 8, 9],
                      [1, 4, 5, 12, 13],
                  ],
              ],
              dtype=torch.float,
          ),
          action=torch.tensor([[0], [1]], dtype=torch.long),
          reward=torch.tensor([[1.5], [2.3]], dtype=torch.float),
      )
  
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 1 for calling the TestLinUCB obj's underlying member method code with example usage: obj.setUp() without return types