parameters: (self, X, y=None)
return_statement: return self
docstring: Fit all transformers using X.
  
          Parameters
          ----------
          X : array-like or DataFrame of shape [n_samples, n_features]
              Input data, of which specified subsets are used to fit the
              transformers.
  
          y : array-like, shape (n_samples, ...), optional
              Targets for supervised learning.
  
          Returns
          -------
          self : ColumnTransformer
              This estimator
docstring_summary: Fit all transformers using X.
docstring_tokens: Fit all transformers using X .
function: def fit(self, X, y=None):
          """Fit all transformers using X.
  
          Parameters
          ----------
          X : array-like or DataFrame of shape [n_samples, n_features]
              Input data, of which specified subsets are used to fit the
              transformers.
  
          y : array-like, shape (n_samples, ...), optional
              Targets for supervised learning.
  
          Returns
          -------
          self : ColumnTransformer
              This estimator
  
          """
          # we use fit_transform to make sure to set sparse_output_ (for which we
          # need the transformed data) to have consistent output type in predict
          self.fit_transform(X, y=y)
          return self
function_tokens: def fit ( self , X , y = None ) : # we use fit_transform to make sure to set sparse_output_ (for which we # need the transformed data) to have consistent output type in predict self . fit_transform ( X , y = y ) return self