docstr: Updates self.quantile_dicts[quantile][feature_vector_in_train] to be
  the quantile of the associated true value bin.
  
  Parameters
  ----------
  feature_vector_in_train: list
       Feature vector that appears in the training data.
  quantile: float
  
  Returns
  -------
  float
      The quantile of the associated true value bin.
docstr_tok: ["Updates","self.quantile_dicts","[","quantile","]","[","feature_vector_in_train","]","to","be","the","quantile","of","the","associated","true","value","bin",".","Parameters","--","--","--","--","--","feature_vector_in_train",":","list","Feature","vector","that","appears","in","the","training","data",".","quantile",":","float","Returns","--","--","--","-","float","The","quantile","of","the","associated","true","value","bin","."]
code: def _get_and_cache_quantile_computation(
      self, feature_vector_in_train: List, quantile: float
  ):
      """
      Updates self.quantile_dicts[quantile][feature_vector_in_train] to be
      the quantile of the associated true value bin.
  
      Parameters
      ----------
      feature_vector_in_train: list
           Feature vector that appears in the training data.
      quantile: float
  
      Returns
      -------
      float
          The quantile of the associated true value bin.
      """
      if feature_vector_in_train not in self.quantile_dicts[quantile]:
          self.quantile_dicts[quantile][
              feature_vector_in_train
          ] = np.percentile(
              self.id_to_bins[
                  self.preds_to_id[feature_vector_in_train]
              ],
              quantile * 100,
          )
      return self.quantile_dicts[quantile][feature_vector_in_train]
  
imports: from typing import Any, Dict, List, Optional, Union
  import copy
  import numpy
  import pandas
  import xgboost
  import gc
  from collections import defaultdict
  from gluonts.core.component import equals, validated
label_desc: Use image node_id 8 for calling the QRX obj's underlying member method code with example usage: obj._get_and_cache_quantile_computation(feature_vector_in_train, quantile) and returns: self