parameters: (self, X, y=None, sample_weight=None)
return_statement: return self
docstring: Fit estimator.
  
          Parameters
          ----------
          X : array-like or sparse matrix, shape=(n_samples, n_features)
              The input samples. Use ``dtype=np.float32`` for maximum
              efficiency. Sparse matrices are also supported, use sparse
              ``csc_matrix`` for maximum efficiency.
  
          sample_weight : array-like of shape (n_samples,), default=None
              Sample weights. If None, then samples are equally weighted. Splits
              that would create child nodes with net zero or negative weight are
              ignored while searching for a split in each node. In the case of
              classification, splits are also ignored if they would result in any
              single class carrying a negative weight in either child node.
  
          Returns
          -------
          self : object
docstring_summary: Fit estimator.
docstring_tokens: Fit estimator .
function: def fit(self, X, y=None, sample_weight=None):
          """
          Fit estimator.
  
          Parameters
          ----------
          X : array-like or sparse matrix, shape=(n_samples, n_features)
              The input samples. Use ``dtype=np.float32`` for maximum
              efficiency. Sparse matrices are also supported, use sparse
              ``csc_matrix`` for maximum efficiency.
  
          sample_weight : array-like of shape (n_samples,), default=None
              Sample weights. If None, then samples are equally weighted. Splits
              that would create child nodes with net zero or negative weight are
              ignored while searching for a split in each node. In the case of
              classification, splits are also ignored if they would result in any
              single class carrying a negative weight in either child node.
  
          Returns
          -------
          self : object
  
          """
          self.fit_transform(X, y, sample_weight=sample_weight)
          return self
function_tokens: def fit ( self , X , y = None , sample_weight = None ) : self . fit_transform ( X , y , sample_weight = sample_weight ) return self