class_docstr: Base class for different image classification models
class_docstr_tok: ["Base","class","for","different","image","classification","models"]
docstr: Run a layer of the model, optionally with checkpointing
docstr_tok: ["Run","a","layer","of","the","model",",","optionally","with","checkpointing"]
code: def _forward_layer(self, layer: nn.Module, x: Tensor) -> Tensor:
      """Run a layer of the model, optionally with checkpointing"""
      # Larger models with large input image size may not be able to fit into memory.
      # We can use gradient checkpointing to enable training with large models and large inputs
      return (
          gradient_checkpoint_fn(layer, x)
          if self.gradient_checkpointing
          else layer(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 5 for calling the BaseImageEncoder obj's underlying member method code with example usage: obj._forward_layer(layer, x) and returns: unknown