code: def test_EmpiricalBayesThompsonSamplerPredict(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=self.Xs,
          # pyre-fixme[6]: For 2nd param expected `List[List[float]]` but got
          #  `List[List[int]]`.
          Ys=self.Ys,
          # pyre-fixme[6]: For 3rd param expected `List[List[float]]` but got
          #  `List[List[int]]`.
          Yvars=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,
      )
      f, cov = generator.predict([[1, 1], [3, 3]])
      self.assertTrue(
          np.allclose(f, np.array([[1.3, 0.25], [2.9, 0.25]]))
      )
  
      # first element of cov is the cov matrix for  the first prediction
      # the element at 0,0 is for the first outcome
      # the element at 1,1 is for the second outcome
      self.assertTrue(
          np.allclose(
              cov,
              np.array(
                  [
                      [[1.03, 0.0], [0.0, 0.375]],
                      [[0.87, 0.0], [0.0, 0.375]],
                  ]
              ),
          )
      )
  
      with self.assertRaises(ValueError):
          generator.predict([[1, 2]])
  
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 6 for calling the EmpiricalBayesThompsonSamplerTest obj's underlying member method code with example usage: obj.test_EmpiricalBayesThompsonSamplerPredict() without return types