class_docstr: A base pre-processing transformer
  
  A subclass of the scikit-learn ``TransformerMixin``, the purpose of the
  ``BaseTransformer`` is to learn characteristics from the training set and
  apply them in a transformation to the test set. For instance, a transformer
  aimed at normalizing features in an exogenous array would learn the means
  and standard deviations of the training features in the ``fit`` method, and
  then center and scale the features in the ``transform`` method.
  
  The ``fit`` method should only ever be applied to the *training* set to
  avoid any data leakage, while ``transform`` may be applied to any dataset
  of the same schema.
docstr: Validate input
docstr_tok: ["Validate","input"]
code: def _check_y_X(y, X):
      """Validate input"""
      # Do not force finite, since a transformer's goal may be imputation.
      if y is not None:
          y = check_endog(
              y,
              dtype=DTYPE,
              copy=True,
              force_all_finite=False,
              preserve_series=False,
          )
  
      if X is not None:
          X = check_exog(
              X,
              dtype=None,
              copy=True,
              force_all_finite=False,
          )
      return y, X
  
file_path: pmdarima/pmdarima/preprocessing/base.py
imports: from sklearn.base import BaseEstimator, TransformerMixin
  import abc
  from ..compat.numpy import DTYPE
  from ..utils import check_exog, check_endog
label_desc: Use image node_id 1 for calling the BaseTransformer obj's underlying member method code with example usage: obj._check_y_X(y, X) and returns: y,  X