parameters: (self, X, y, sample_weight=None)
return_statement: return super().fit(X, y, sample_weight)
docstring: Fit the estimators.
  
          Parameters
          ----------
          X : {array-like, sparse matrix}, shape (n_samples, n_features)
              Training vectors, where n_samples is the number of samples and
              n_features is the number of features.
  
          y : array-like, shape (n_samples,)
              Target values.
  
          sample_weight : array-like, shape (n_samples,) or None
              Sample weights. If None, then samples are equally weighted.
              Note that this is supported only if all underlying estimators
              support sample weights.
  
          Returns
          -------
          self : object
              Fitted estimator.
docstring_summary: Fit the estimators.
docstring_tokens: Fit the estimators .
function: def fit(self, X, y, sample_weight=None):
          """Fit the estimators.
  
          Parameters
          ----------
          X : {array-like, sparse matrix}, shape (n_samples, n_features)
              Training vectors, where n_samples is the number of samples and
              n_features is the number of features.
  
          y : array-like, shape (n_samples,)
              Target values.
  
          sample_weight : array-like, shape (n_samples,) or None
              Sample weights. If None, then samples are equally weighted.
              Note that this is supported only if all underlying estimators
              support sample weights.
  
          Returns
          -------
          self : object
              Fitted estimator.
          """
          y = column_or_1d(y, warn=True)
          return super().fit(X, y, sample_weight)
function_tokens: def fit ( self , X , y , sample_weight = None ) : y = column_or_1d ( y , warn = True ) return super ( ) . fit ( X , y , sample_weight )