class_docstr: A default Linear Classifier based on sklearn's SGDClassifier for
  learning decision boundaries between concepts.
  Note that default implementation slices input dataset into train and test
  splits and keeps them in memory.
  In case concept datasets are large, this can lead to out of memory and we
  recommend to provide a custom Classier that extends `Classifier` abstract
  class and handles large concept datasets accordingly.
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docstr: This function returns the list of all classes that are used by the
  classifier to train the model in the `train_and_eval` method.
  The order of returned classes has to match the same order used in
  the weights matrix returned by the `weights` method.
  
  Returns:
      classes (list): The list of classes used by the classifier to train
      the model in the `train_and_eval` method.
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code: def classes(self) -> List[int]:
      r"""
      This function returns the list of all classes that are used by the
      classifier to train the model in the `train_and_eval` method.
      The order of returned classes has to match the same order used in
      the weights matrix returned by the `weights` method.
  
      Returns:
          classes (list): The list of classes used by the classifier to train
          the model in the `train_and_eval` method.
      """
      return self.lm.classes().detach().numpy()
  
imports: import random
  import warnings
  from abc import ABC, abstractmethod
  from typing import Any, Dict, List, Tuple, Union
  import torch
  from captum._utils.models.linear_model import model
  from torch import Tensor
  from torch.utils.data import DataLoader, TensorDataset
label_desc: Use image node_id 4 for calling the DefaultClassifier obj's underlying member method code with example usage: obj.classes() and returns: self