code: def fit(self, X, y=None):
          
          # default to QuicGraphicalLassoCV
          estimator = self.estimator or QuicGraphicalLassoCV()
  
          self.lam_ = None
          self.estimator_ = None
  
          X = check_array(X, ensure_min_features=2, estimator=self)
          X = as_float_array(X, copy=False, force_all_finite=False)
  
          n_samples_, n_features_ = X.shape
  
          # perform first estimate
          estimator.fit(X)
  
          if self.method == "binary":
              # generate weights
              self.lam_ = self._binary_weights(estimator)
  
              # perform second step adaptive estimate
              self.estimator_ = QuicGraphicalLasso(
                  lam=self.lam_ * estimator.lam_,
                  mode="default",
                  init_method="cov",
                  auto_scale=False,
              )
              self.estimator_.fit(X)
  
          elif self.method == "inverse_squared":
              self.lam_ = self._inverse_squared_weights(estimator)
  
              # perform second step adaptive estimate
              self.estimator_ = QuicGraphicalLassoCV(
                  lam=self.lam_ * self.estimator.lam_, auto_scale=False
              )
              self.estimator_.fit(X)
  
          elif self.method == "inverse":
              self.lam_ = self._inverse_weights(estimator)
  
              # perform second step adaptive estimate
              self.estimator_ = QuicGraphicalLassoCV(
                  lam=self.lam_ * estimator.lam_, auto_scale=False
              )
              self.estimator_.fit(X)
  
          else:
              raise NotImplementedError(
                  (
                      "Only method='binary', 'inverse_squared', or",
                      "'inverse' have been implemented.",
                  )
              )
  
          self.is_fitted_ = True
          return self
docs: Estimate the precision using an adaptive maximum likelihood estimator.
          Parameters
          ----------
          X : ndarray, shape (n_samples, n_features)
              Data from which to compute the proportion matrix.