class_docstr: Generator for Thompson sampling using Empirical Bayes estimates.
  
  The generator applies positive-part James-Stein Estimator to the data
  passed in via `fit` and then performs Thompson Sampling.
code: def _fit_Ys_and_Yvars(
      self,
      Ys: List[List[float]],
      Yvars: List[List[float]],
      outcome_names: List[str],
  ) -> Tuple[List[List[float]], List[List[float]]]:
      newYs = []
      newYvars = []
      for i, (Y, Yvar) in enumerate(zip(Ys, Yvars)):
          newY, newYvar = self._apply_shrinkage(Y, Yvar, i)
          newYs.append(newY)
          newYvars.append(newYvar)
      return newYs, newYvars
  
file_path: Ax/ax/models/discrete/eb_thompson.py
imports: import logging
  from typing import List, Tuple
  import numpy
  from ax.models.discrete.thompson import ThompsonSampler
  from ax.utils.common.logger import get_logger
  from ax.utils.stats.statstools import positive_part_james_stein
label_desc: Use image node_id 1 for calling the EmpiricalBayesThompsonSampler obj's underlying member method code with example usage: obj._fit_Ys_and_Yvars(Ys, Yvars, outcome_names) and returns: newYs,  newYvars