code: def fit(self, X, y, coef_init=None, intercept_init=None,
              sample_weight=None):
          
          super(SGDClassifier, self).fit(X, y, coef_init, intercept_init,
                                         sample_weight)
docs: Fit linear model with Stochastic Gradient Descent.
  
          Parameters
          ----------
          X : {array-like, sparse matrix}, shape (n_samples, n_features)
              Training data
  
          y : numpy array, shape (n_samples,)
              Target values
  
          coef_init : array, shape (n_classes, n_features)
              The initial coefficients to warm-start the optimization.
  
          intercept_init : array, shape (n_classes,)
              The initial intercept to warm-start the optimization.
  
          sample_weight : array-like, shape (n_samples,), optional
              Weights applied to individual samples.
              If not provided, uniform weights are assumed. These weights will
              be multiplied with class_weight (passed through the
              constructor) if class_weight is specified
  
          Returns
          -------
          self : returns an instance of self.