code: def test_min_explore_steps(self) -> None:
      # test min number of exploration steps
      min_explore_steps = 3
      bandit = UCB1MAB(min_explore_steps=min_explore_steps)
      policy = Policy(
          scorer=bandit,
          sampler=GreedyActionSampler(),
      )
      trainer = MABTrainer(policy)
      batch = replace(self.batch, weight=torch.Tensor([[2.9], [3.1]]))
      trainer.cb_training_step(batch, 0, 0)
  
      pred_arm_ids = torch.tensor([[3, 4]]).long()
      model_output = bandit(pred_arm_ids)
  
      ucb = model_output["ucb"].numpy()
      # check that we return default_score (inf) for arm which wasn't sufficiently explored
      self.assertEqual(ucb[0, 0], float("inf"))
  
      # check that we return finite for arm which was sufficiently explored
      self.assertLess(ucb[0, 1], float("inf"))
  
file_path: ReAgent/reagent/test/models/test_mab.py
imports: import unittest
  from dataclasses import replace
  import numpy
  import numpy.testing
  import torch
  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.mab import UCB1MAB
  from reagent.training.cb.mab_trainer import MABTrainer
label_desc: Use image node_id 4 for calling the TestUCB1MAB obj's underlying member method code with example usage: obj.test_min_explore_steps() without return types