class_docstr: Allow using an estimator as a transformer in an earlier step of a pipeline.
  
  !!! warning
      By default all the checks on the inputs `X` and `y` are delegated to the wrapped estimator.
  
      To change such behaviour, set `check_input` to `True`.
  
  Parameters
  ----------
  estimator : scikit-learn compatible estimator
      The estimator to be applied to the data, used as transformer.
  predict_func : str, default="predict"
      The method called on the estimator when transforming e.g. (`"predict"`, `"predict_proba"`).
  check_input : bool, default=False
      Whether or not to check the input data. If False, the checks are delegated to the wrapped estimator.
  
  Attributes
  ----------
  estimator_ : scikit-learn compatible estimator
      The fitted underlying estimator.
  multi_output_ : bool
      Whether or not the estimator is multi output.
docstr: Fit the underlying estimator on training data `X` and `y`.
  
  Parameters
  ----------
  X : array-like of shape (n_samples, n_features)
      Training data.
  y : array-like of shape (n_samples,)
      Target values.
  **kwargs : dict
      Additional keyword arguments passed to the `fit` method of the underlying estimator.
  
  Returns
  -------
  self : EstimatorTransformer
      The fitted transformer.
code: def fit(self, X, y, **kwargs):
      """Fit the underlying estimator on training data `X` and `y`.
  
      Parameters
      ----------
      X : array-like of shape (n_samples, n_features)
          Training data.
      y : array-like of shape (n_samples,)
          Target values.
      **kwargs : dict
          Additional keyword arguments passed to the `fit` method of the underlying estimator.
  
      Returns
      -------
      self : EstimatorTransformer
          The fitted transformer.
      """
  
      if self.check_input:
          X, y = check_X_y(
              X,
              y,
              estimator=self,
              dtype=FLOAT_DTYPES,
              multi_output=True,
          )
  
      self.multi_output_ = len(y.shape) > 1
      self.estimator_ = clone(self.estimator)
      self.estimator_.fit(X, y, **kwargs)
      return self
  
file_path: scikit-lego/sklego/meta/estimator_transformer.py
imports: from sklearn import clone
  from sklearn.base import BaseEstimator, MetaEstimatorMixin, TransformerMixin
  from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted, check_X_y
label_desc: Use image node_id 2 for calling the EstimatorTransformer obj's underlying member method code with example usage: obj.fit(X, y) and returns: self