parameters: (self, X, y=None)
return_statement: return self.fit(X, y).transform(X, y)
docstring: Learn and apply the dimension reduction on the train data.
  
          Parameters
          ----------
          X : array-like of shape (n_samples, n_features)
              Training vectors, where n_samples is the number of samples and
              n_features is the number of predictors.
  
          y : array-like of shape (n_samples, n_targets)
              Target vectors, where n_samples is the number of samples and
              n_targets is the number of response variables.
  
          Returns
          -------
          x_scores if Y is not given, (x_scores, y_scores) otherwise.
docstring_summary: Learn and apply the dimension reduction on the train data.
docstring_tokens: Learn and apply the dimension reduction on the train data .
function: def fit_transform(self, X, y=None):
          """Learn and apply the dimension reduction on the train data.
  
          Parameters
          ----------
          X : array-like of shape (n_samples, n_features)
              Training vectors, where n_samples is the number of samples and
              n_features is the number of predictors.
  
          y : array-like of shape (n_samples, n_targets)
              Target vectors, where n_samples is the number of samples and
              n_targets is the number of response variables.
  
          Returns
          -------
          x_scores if Y is not given, (x_scores, y_scores) otherwise.
          """
          return self.fit(X, y).transform(X, y)
function_tokens: def fit_transform ( self , X , y = None ) : return self . fit ( X , y ) . transform ( X , y )