class_docstr: Base class for different image classification models
class_docstr_tok: ["Base","class","for","different","image","classification","models"]
docstr: A forward function of the model, optionally training the model with
  neural augmentation.
docstr_tok: ["A","forward","function","of","the","model",",","optionally","training","the","model","with","neural","augmentation","."]
code: def forward(self, x: Any, *args, **kwargs) -> Any:
      """A forward function of the model, optionally training the model with
      neural augmentation."""
      if self.neural_augmentor is not None:
          if self.training:
              x_aug = self.neural_augmentor(x)
              prediction = self.forward_classifier(x_aug)  # .detach()
              out_dict = {
                  "augmented_tensor": x_aug,
                  "logits": prediction,
              }
          else:
              out_dict = {
                  "augmented_tensor": None,
                  "logits": self.forward_classifier(x),
              }
          return out_dict
      else:
          x = self.forward_classifier(x, *args, **kwargs)
          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 10 for calling the BaseImageEncoder obj's underlying member method code with example usage: obj.forward(x) and returns: out_dict, x