code: def test_EmpiricalBayesThompsonSamplerWarning(self) -> None:
      generator = EmpiricalBayesThompsonSampler(min_weight=0.0)
      generator.fit(
          # pyre-fixme[6]: For 1st param expected `List[List[List[Union[None,
          #  bool, float, int, str]]]]` but got `List[List[List[int]]]`.
          Xs=[x[:-1] for x in self.Xs],
          # pyre-fixme[6]: For 2nd param expected `List[List[float]]` but got
          #  `List[List[int]]`.
          Ys=[y[:-1] for y in self.Ys],
          # pyre-fixme[6]: For 3rd param expected `List[List[float]]` but got
          #  `List[List[int]]`.
          Yvars=[y[:-1] for y in self.Yvars],
          # pyre-fixme[6]: For 4th param expected `List[List[Union[None, bool,
          #  float, int, str]]]` but got `List[List[int]]`.
          parameter_values=self.parameter_values,
          outcome_names=self.outcome_names,
      )
      arms, weights, _ = generator.gen(
          n=5,
          # pyre-fixme[6]: For 2nd param expected `List[List[Union[None, bool,
          #  float, int, str]]]` but got `List[List[int]]`.
          parameter_values=self.parameter_values,
          objective_weights=np.array([1, 0]),
      )
      self.assertEqual(arms, [[3, 3], [2, 2], [1, 1]])
      for weight, expected_weight in zip(
          weights, [3 * i for i in [0.74, 0.21, 0.05]]
      ):
          self.assertAlmostEqual(weight, expected_weight, delta=0.1)
  
file_path: Ax/ax/models/tests/test_eb_thompson.py
imports: from unittest.mock import patch
  import numpy
  from ax.models.discrete.eb_thompson import EmpiricalBayesThompsonSampler
  from ax.utils.common.testutils import TestCase
label_desc: Use image node_id 4 for calling the EmpiricalBayesThompsonSamplerTest obj's underlying member method code with example usage: obj.test_EmpiricalBayesThompsonSamplerWarning() without return types