code: def n_nonzero(self, percentage=False):
      if hasattr(self, "coef_"):
          coef = self.coef_
      else:
          coef = self.dual_coef_
  
      n_nz = np.sum(np.sum(coef != 0, axis=0, dtype=bool))
  
      if percentage:
          if (
              hasattr(self, "support_vectors_")
              and self.support_vectors_ is not None
          ):
              n_nz /= self.n_samples_
          else:
              n_nz /= coef.shape[1]
  
      return n_nz
  
imports: import numpy
  import scipy.sparse
  import scipy.special
  from sklearn.base import BaseEstimator
  from sklearn.base import ClassifierMixin, RegressorMixin
  from sklearn.utils.extmath import safe_sparse_dot
  from sklearn.preprocessing import LabelBinarizer
  from sklearn.preprocessing import LabelEncoder
  from .randomkit import RandomState
label_desc: Use image node_id 2 for calling the BaseEstimator obj's underlying member method code with example usage: obj.n_nonzero(percentage) and returns: n_nz