docstr: Return appropriate training X and y values depending on whether
  X_train and y_train are passed or not. If X_train and y_train are not
  passes, internal self.X_train and self.y_train are returned. If they are
  passed, then a copy of them is returned.
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code: def _create_model_get_train_X_y(self, X_train, y_train):
      """Return appropriate training X and y values depending on whether
      X_train and y_train are passed or not. If X_train and y_train are not
      passes, internal self.X_train and self.y_train are returned. If they are
      passed, then a copy of them is returned.
      """
      if X_train is not None:
          data_X = X_train.copy()
      else:
          if self.X_train is None:
              data_X = None
          else:
              data_X = self.X_train
      data_y = self.y_train if y_train is None else y_train.copy()
      return data_X, data_y
  
file_path: pycaret/pycaret/internal/pycaret_experiment/non_ts_supervised_experiment.py
imports: import pandas
  from pycaret.internal.pycaret_experiment.supervised_experiment import _SupervisedExperiment
label_desc: Use image node_id 15 for calling the _NonTSSupervisedExperiment obj's underlying member method code with example usage: obj._create_model_get_train_X_y(X_train, y_train) and returns: data_X,  data_y