class_docstr: Base class providing API and common functions for all multi-label
  classifiers.
  
  Implements base functionality for ML classifiers, especially the get/set params for
  scikit-learn compatibility.
  
  Attributes
  ----------
  copyable_attrs : List[str]
      list of attribute names that should be copied when class is cloned
docstr: Abstract method to fit classifier with training data
  
  It must return a fitted instance of :code:`self`.
  
  Parameters
  ----------
  X : numpy.ndarray or scipy.sparse
      input features, can be a dense or sparse matrix of size
      :code:`(n_samples, n_features)`
  y : numpy.ndaarray or scipy.sparse {0,1}
      binary indicator matrix with label assignments.
  
  Returns
  -------
  object
      fitted instance of self
  
  Raises
  ------
  NotImplementedError
      this is just an abstract method
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code: def fit(self, X, y):
      """Abstract method to fit classifier with training data
  
      It must return a fitted instance of :code:`self`.
  
      Parameters
      ----------
      X : numpy.ndarray or scipy.sparse
          input features, can be a dense or sparse matrix of size
          :code:`(n_samples, n_features)`
      y : numpy.ndaarray or scipy.sparse {0,1}
          binary indicator matrix with label assignments.
  
      Returns
      -------
      object
          fitted instance of self
  
      Raises
      ------
      NotImplementedError
          this is just an abstract method
      """
  
      raise NotImplementedError("MLClassifierBase::fit()")
  
file_path: scikit-multilearn/skmultilearn/base/base.py
imports: import numpy
  from ..utils import get_matrix_in_format, matrix_creation_function_for_format
  from scipy.sparse import issparse
  from sklearn.base import BaseEstimator, ClassifierMixin
label_desc: Use image node_id 5 for calling the MLClassifierBase obj's underlying member method code with example usage: obj.fit(X, y) without return types