class_docstr: Generator for Thompson sampling.
  
  The generator performs Thompson sampling on the data passed in via `fit`.
  Arms are given weight proportional to the probability that they are
  winners, according to Monte Carlo simulations.
class_docstr_tok: ["Generator","for","Thompson","sampling",".","The","generator","performs","Thompson","sampling","on","the","data","passed","in","via","`","fit","`",".","Arms","are","given","weight","proportional","to","the","probability","that","they","are","winners",",","according","to","Monte","Carlo","simulations","."]
docstr: After validation has been performed, it's safe to use Xs[0].
docstr_tok: ["After","validation","has","been","performed",",","it","'s","safe","to","use","Xs","[","0","]","."]
code: def _fit_X(
      self, Xs: List[List[TParamValueList]]
  ) -> List[TParamValueList]:
      """After validation has been performed, it's safe to use Xs[0]."""
      self._validate_Xs(Xs=Xs)
      return Xs[0]
  
imports: import hashlib
  import json
  from typing import Dict, List, Optional, Tuple
  import numpy
  from ax.core.types import TGenMetadata, TParamValue, TParamValueList
  from ax.exceptions.constants import TS_MIN_WEIGHT_ERROR, TS_NO_FEASIBLE_ARMS_ERROR
  from ax.exceptions.model import ModelError
  from ax.models.discrete_base import DiscreteModel
  from ax.models.types import TConfig
  from ax.utils.common.docutils import copy_doc
label_desc: Use image node_id 9 for calling the ThompsonSampler obj's underlying member method code with example usage: obj._fit_X(Xs) and returns: Xs