class_docstr: Removes outliers (train-time only) using the supplied removal model.
  
  Parameters
  ----------
  outlier_detector : object
      An outlier detector that implements `fit` and `predict` methods.
  refit : bool, default=True
      If True, fits the estimator during pipeline.fit(). If False, the estimator is not fitted during pipeline.fit().
  
  Attributes
  ----------
  estimator_ : object
      The fitted outlier detector.
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docstr: Fits the estimator, and the outlier detector if `refit` is True.
  
  Parameters
  ----------
  X : array-like of shape (n_samples, n_features)
      Training data.
  y : array-like of shape (n_samples,), default=None
      Target values.
  
  Returns
  -------
  self : OutlierRemover
      The fitted transformer.
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code: def fit(self, X, y=None):
      """Fits the estimator, and the outlier detector if `refit` is True.
  
      Parameters
      ----------
      X : array-like of shape (n_samples, n_features)
          Training data.
      y : array-like of shape (n_samples,), default=None
          Target values.
  
      Returns
      -------
      self : OutlierRemover
          The fitted transformer.
      """
      self.estimator_ = clone(self.outlier_detector)
      if self.refit:
          super().fit(X, y)
          self.estimator_.fit(X, y)
      return self
  
file_path: scikit-lego/sklego/meta/outlier_remover.py
imports: from deprecated import deprecated
  from sklearn import clone
  from sklearn.base import BaseEstimator
  from sklearn.utils.validation import check_array, check_is_fitted
  from sklego.common import TrainOnlyTransformerMixin
label_desc: Use image node_id 2 for calling the OutlierRemover obj's underlying member method code with example usage: obj.fit(X, y) and returns: self