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: Fit the transformer
  
  The purpose of the ``fit`` method is to learn a set of statistics or
  characteristics from the training set, and store them as "fit
  attributes" within the instance. A transformer *must* be fit before
  the transformation can be applied to a dataset in the ``transform``
  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.
  
  Returns
  -------
  self : BaseTransformer
      The scikit-learn convention is for the ``fit`` method to return
      the instance of the transformer, ``self``. This allows us to
      string ``fit(...).transform(...)`` calls together.
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code: def fit(self, y, X):
      """Fit the transformer
  
      The purpose of the ``fit`` method is to learn a set of statistics or
      characteristics from the training set, and store them as "fit
      attributes" within the instance. A transformer *must* be fit before
      the transformation can be applied to a dataset in the ``transform``
      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.
  
      Returns
      -------
      self : BaseTransformer
          The scikit-learn convention is for the ``fit`` method to return
          the instance of the transformer, ``self``. This allows us to
          string ``fit(...).transform(...)`` calls together.
      """
  
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 3 for calling the BaseTransformer obj's underlying member method code with example usage: obj.fit(y, X) without return types