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: Transform the new array
  
  Apply the transformation to the array after learning the training set's
  characteristics in the ``fit`` method.
  
  Parameters
  ----------
  y : array-like or None, shape=(n_samples,)
      The endogenous (time-series) array.
  
  X : array-like or None, shape=(n_samples, n_features)
      The exogenous array of additional covariates.
  
  **kwargs : keyword args
      Keyword arguments required by the transform function.
  
  Returns
  -------
  y : array-like or None
      The transformed y array
  
  X : array-like or None
      The transformed X array
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code: def transform(self, y, X, **kwargs):
      """Transform the new array
  
      Apply the transformation to the array after learning the training set's
      characteristics in the ``fit`` method.
  
      Parameters
      ----------
      y : array-like or None, shape=(n_samples,)
          The endogenous (time-series) array.
  
      X : array-like or None, shape=(n_samples, n_features)
          The exogenous array of additional covariates.
  
      **kwargs : keyword args
          Keyword arguments required by the transform function.
  
      Returns
      -------
      y : array-like or None
          The transformed y array
  
      X : array-like or None
          The transformed X array
      """
  
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 4 for calling the BaseTransformer obj's underlying member method code with example usage: obj.transform(y, X) without return types