class_docstr: Transformers that create new exog features from the endog or exog array
  
  Parameters
  ----------
  prefix : str or None, optional (default=None)
      The feature prefix
docstr: Transform the new array
  
  Apply the transformation to the array after learning the training set's
  characteristics in the ``fit`` method. The transform method for
  featurizers behaves slightly differently in that the ``n_periods` may
  be required to extrapolate for periods in the future.
  
  Parameters
  ----------
  y : array-like or None, shape=(n_samples,)
      The endogenous (time-series) array.
  
  X : array-like or None, shape=(n_samples, n_features)
      An array of additional covariates.
  
  n_periods : int, optional (default=0)
      The number of periods in the future to forecast. If ``n_periods``
      is 0, will compute the features for the training set.
      ``n_periods`` corresponds to the number of samples that will be
      returned.
  
  **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=None, n_periods=0, **kwargs):
      """Transform the new array
  
      Apply the transformation to the array after learning the training set's
      characteristics in the ``fit`` method. The transform method for
      featurizers behaves slightly differently in that the ``n_periods` may
      be required to extrapolate for periods in the future.
  
      Parameters
      ----------
      y : array-like or None, shape=(n_samples,)
          The endogenous (time-series) array.
  
      X : array-like or None, shape=(n_samples, n_features)
          An array of additional covariates.
  
      n_periods : int, optional (default=0)
          The number of periods in the future to forecast. If ``n_periods``
          is 0, will compute the features for the training set.
          ``n_periods`` corresponds to the number of samples that will be
          returned.
  
      **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/exog/base.py
imports: import pandas
  import numpy
  import abc
  from ..base import BaseTransformer
label_desc: Use image node_id 5 for calling the BaseExogFeaturizer obj's underlying member method code with example usage: obj.transform(y, X, n_periods) without return types