class_docstr: An abstract class definition of any classifier that allows to train a model
  and access trained weights of that model.
  
  More specifically the classifier can, for instance, be trained on the
  activations of a particular layer. Below we can see an example a sklearn
  linear classifier wrapped by the `CustomClassifier` which extends `Classifier`
  abstract class.
  
  Example::
  
  >>> from sklearn import linear_model
  >>>
  >>> class CustomClassifier(Classifier):
  >>>
  >>> def __init__(self):
  >>>
  >>>     self.lm = linear_model.SGDClassifier(alpha=0.01, max_iter=1000,
  >>>                                          tol=1e-3)
  >>>
  >>> def train_and_eval(self, dataloader):
  >>>
  >>>     x_train, x_test, y_train, y_test = train_test_split(inputs, labels)
  >>>     self.lm.fit(x_train.detach().numpy(), y_train.detach().numpy())
  >>>
  >>>     preds = torch.tensor(self.lm.predict(x_test.detach().numpy()))
  >>>     return {'accs': (preds == y_test).float().mean()}
  >>>
  >>>
  >>> def weights(self):
  >>>
  >>>     if len(self.lm.coef_) == 1:
  >>>         # if there are two concepts, there is only one label.
  >>>         # We split it in two.
  >>>         return torch.tensor([-1 * self.lm.coef_[0], self.lm.coef_[0]])
  >>>     else:
  >>>         return torch.tensor(self.lm.coef_)
  >>>
  >>>
  >>> def classes(self):
  >>>     return self.lm.classes_
  >>>
  >>>
class_docstr_tok: ["An","abstract","class","definition","of","any","classifier","that","allows","to","train","a","model","and","access","trained","weights","of","that","model",".","More","specifically","the","classifier","can",",","for","instance",",","be","trained","on","the","activations","of","a","particular","layer",".","Below","we","can","see","an","example","a","sklearn","linear","classifier","wrapped","by","the","`","CustomClassifier","`","which","extends","`","Classifier","`","abstract","class",".","Example",":",":",">",">",">","from","sklearn","import","linear_model",">",">",">",">",">",">","class","CustomClassifier","(","Classifier",")",":",">",">",">",">",">",">","def","__init__","(","self",")",":",">",">",">",">",">",">","self.lm","=","linear_model.SGDClassifier","(","alpha=0.01",",","max_iter=1000",",",">",">",">","tol=1e-3",")",">",">",">",">",">",">","def","train_and_eval","(","self",",","dataloader",")",":",">",">",">",">",">",">","x_train",",","x_test",",","y_train",",","y_test","=","train_test_split","(","inputs",",","labels",")",">",">",">","self.lm.fit","(","x_train.detach","(",")",".numpy","(",")",",","y_train.detach","(",")",".numpy","(",")",")",">",">",">",">",">",">","preds","=","torch.tensor","(","self.lm.predict","(","x_test.detach","(",")",".numpy","(",")",")",")",">",">",">","return","{","'accs","'",":","(","preds","==","y_test",")",".float","(",")",".mean","(",")","}",">",">",">",">",">",">",">",">",">","def","weights","(","self",")",":",">",">",">",">",">",">","if","len","(","self.lm.coef_",")","==","1",":",">",">",">","#","if","there","are","two","concepts",",","there","is","only","one","label",".",">",">",">","#","We","split","it","in","two",".",">",">",">","return","torch.tensor","(","[","-1","*","self.lm.coef_","[","0","]",",","self.lm.coef_","[","0","]","]",")",">",">",">","else",":",">",">",">","return","torch.tensor","(","self.lm.coef_",")",">",">",">",">",">",">",">",">",">","def","classes","(","self",")",":",">",">",">","return","self.lm.classes_",">",">",">",">",">",">"]
docstr: This method is responsible for training a classifier using the data
  provided through `dataloader` input arguments. Based on the specific
  implementation, it may or may not return a statistics about model
  training and evaluation.
  
  Args:
      dataloader (dataloader): A dataloader that enables batch-wise access to
              the inputs and corresponding labels. Dataloader allows us to
              iterate over the dataset by loading the batches in lazy manner.
      kwargs  (dict): Named arguments that are used for training and evaluating
              concept classifier.
              Default: None
  Returns:
      stats (dict): a dictionary of statistics about the performance of the model.
              For example the accuracy of the model on the test and/or
              train dataset(s). The user may decide to return None or an
              empty dictionary if they decide to not return any performance
              statistics.
docstr_tok: ["This","method","is","responsible","for","training","a","classifier","using","the","data","provided","through","`","dataloader","`","input","arguments",".","Based","on","the","specific","implementation",",","it","may","or","may","not","return","a","statistics","about","model","training","and","evaluation",".","Args",":","dataloader","(","dataloader",")",":","A","dataloader","that","enables","batch-wise","access","to","the","inputs","and","corresponding","labels",".","Dataloader","allows","us","to","iterate","over","the","dataset","by","loading","the","batches","in","lazy","manner",".","kwargs","(","dict",")",":","Named","arguments","that","are","used","for","training","and","evaluating","concept","classifier",".","Default",":","None","Returns",":","stats","(","dict",")",":","a","dictionary","of","statistics","about","the","performance","of","the","model",".","For","example","the","accuracy","of","the","model","on","the","test","and\/or","train","dataset","(","s",")",".","The","user","may","decide","to","return","None","or","an","empty","dictionary","if","they","decide","to","not","return","any","performance","statistics","."]
code: def train_and_eval(
      self, dataloader: DataLoader, **kwargs: Any
  ) -> Union[Dict, None]:
      r"""
      This method is responsible for training a classifier using the data
      provided through `dataloader` input arguments. Based on the specific
      implementation, it may or may not return a statistics about model
      training and evaluation.
  
      Args:
          dataloader (dataloader): A dataloader that enables batch-wise access to
                  the inputs and corresponding labels. Dataloader allows us to
                  iterate over the dataset by loading the batches in lazy manner.
          kwargs  (dict): Named arguments that are used for training and evaluating
                  concept classifier.
                  Default: None
      Returns:
          stats (dict): a dictionary of statistics about the performance of the model.
                  For example the accuracy of the model on the test and/or
                  train dataset(s). The user may decide to return None or an
                  empty dictionary if they decide to not return any performance
                  statistics.
      """
      pass
  
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 2 for calling the Classifier obj's underlying member method code with example usage: obj.train_and_eval(dataloader) without return types