docstr: Quantile prediction.
  
  Parameters
  ----------
  x_test: pd.DataFrame if self.x_train_is_dataframe, else list of
  lists
  quantile: float
  
  Returns
  -------
  list
      list of floats
docstr_tok: ["Quantile","prediction",".","Parameters","--","--","--","--","--","x_test",":","pd.DataFrame","if","self.x_train_is_dataframe",",","else","list","of","lists","quantile",":","float","Returns","--","--","--","-","list","list","of","floats"]
code: def predict(
      self, x_test: Union[pd.DataFrame, List], quantile: float
  ) -> List:
      """
      Quantile prediction.
  
      Parameters
      ----------
      x_test: pd.DataFrame if self.x_train_is_dataframe, else list of
      lists
      quantile: float
  
      Returns
      -------
      list
          list of floats
      """
      if self.x_train_is_dataframe:
          preds = self.model.predict(x_test)
          predicted_values = [
              self._get_and_cache_quantile_computation(
                  self.get_closest_pt(self.sorted_train_preds, pred),  # type: ignore
                  quantile,
              )
              for pred in preds
          ]
      else:
          predicted_values = []
          for pt in x_test:
              pred = self.model.predict(np.array([pt]))[
                  0
              ]  # xgboost doesn't like lists
              closest_pred = self.get_closest_pt(
                  self.sorted_train_preds, pred
              )
              predicted_values.append(
                  self._get_and_cache_quantile_computation(
                      closest_pred, quantile  # type: ignore
                  )
              )
      return predicted_values
  
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 9 for calling the QRX obj's underlying member method code with example usage: obj.predict(x_test, quantile) and returns: predicted_values