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.
class_docstr_tok: ["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","."]
code: def __init__(self) -> None:
      warnings.warn(
          "Using default classifier for TCAV which keeps input"
          " both train and test datasets in the memory. Consider defining"
          " your own classifier that doesn't rely heavily on memory, for"
          " large number of concepts, by extending"
          " `Classifer` abstract class"
      )
      self.lm = model.SkLearnSGDClassifier(
          alpha=0.01, max_iter=1000, tol=1e-3
      )
  
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 1 to create a new DefaultClassifier object from inherited base classes: Classifier with example: obj = DefaultClassifier()