class_docstr: Base class for different image classification models
class_docstr_tok: ["Base","class","for","different","image","classification","models"]
docstr: A helper function to extract features and running a classifier.
docstr_tok: ["A","helper","function","to","extract","features","and","running","a","classifier","."]
code: def forward_classifier(self, x: Tensor, *args, **kwargs) -> Tensor:
      """A helper function to extract features and running a classifier."""
      # We add another classifier function so that the classifiers
      # that do not adhere to the structure of BaseEncoder can still
      # use neural augmentor
      x = self.extract_features(x)
      x = self.classifier(x)
      return x
  
imports: import argparse
  from typing import Any, Dict, List, Mapping, Optional, Tuple
  import torch
  from torch import Tensor, nn
  from torch.utils.checkpoint import checkpoint
  from common import is_test_env
  from cvnets.layers import LinearLayer
  from cvnets.misc.init_utils import initialize_fc_layer
  from cvnets.models import MODEL_REGISTRY, BaseAnyNNModel
  from cvnets.neural_augmentor import build_neural_augmentor
  from utils import logger
label_desc: Use image node_id 9 for calling the BaseImageEncoder obj's underlying member method code with example usage: obj.forward_classifier(x) and returns: x