docstr: Fit the model according to the given training data.
  
  Parameters
  ----------
  X : {array-like, sparse matrix} of shape (n_samples, n_features)
      Training vector, where n_samples is the number of samples and
      n_features is the number of features.
  
  y : array-like of shape (n_samples,)
      Target values.
  
  **fit_params : dict
      Parameters passed to the ``fit`` method of the underlying
      regressor.
  
  
  Returns
  -------
  self : object
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code: def fit(self, X, y, **fit_params):
      """Fit the model according to the given training data.
  
      Parameters
      ----------
      X : {array-like, sparse matrix} of shape (n_samples, n_features)
          Training vector, where n_samples is the number of samples and
          n_features is the number of features.
  
      y : array-like of shape (n_samples,)
          Target values.
  
      **fit_params : dict
          Parameters passed to the ``fit`` method of the underlying
          regressor.
  
  
      Returns
      -------
      self : object
      """
  
      # workaround - for some reason, if data is in float32, super().fit() will return an array of 0s
      # this also happens in pure scikit-learn
      y = y.astype("float64")
  
      super().fit(X, y, **fit_params)
      return self
  
imports: import sklearn.compose
  from sklearn.base import BaseEstimator, ClassifierMixin, clone
  from sklearn.compose import TransformedTargetRegressor
  from sklearn.preprocessing import PowerTransformer
  from sklearn.utils.validation import check_is_fitted
label_desc: Use image node_id 3 for calling the PowerTransformedTargetRegressor obj's underlying member method code with example usage: obj.fit(X, y) and returns: self