parameters: (self, X, y=None)
return_statement: return self.fit(X).transform(X)
docstring: Fit to data, then transform it.
  
          Fits transformer to X and y with optional parameters fit_params
          and returns a transformed version of X.
  
          Parameters
          ----------
          X : array-like of shape (n_samples, n_features)
              Training set.
  
          y : ignored
  
          Returns
          -------
          Xt : CSR sparse graph of shape (n_samples, n_samples)
              Xt[i, j] is assigned the weight of edge that connects i to j.
              Only the neighbors have an explicit value.
              The diagonal is always explicit.
docstring_summary: Fit to data, then transform it.
docstring_tokens: Fit to data then transform it .
function: def fit_transform(self, X, y=None):
          """Fit to data, then transform it.
  
          Fits transformer to X and y with optional parameters fit_params
          and returns a transformed version of X.
  
          Parameters
          ----------
          X : array-like of shape (n_samples, n_features)
              Training set.
  
          y : ignored
  
          Returns
          -------
          Xt : CSR sparse graph of shape (n_samples, n_samples)
              Xt[i, j] is assigned the weight of edge that connects i to j.
              Only the neighbors have an explicit value.
              The diagonal is always explicit.
          """
          return self.fit(X).transform(X)
function_tokens: def fit_transform ( self , X , y = None ) : return self . fit ( X ) . transform ( X )