class_docstr: Base class for different image classification models
class_docstr_tok: ["Base","class","for","different","image","classification","models"]
docstr: This function can be used to prepare the architecture for inference. For example,
  re-parameterizing branches when possible. The functionality of this method may vary
  from model to model, so child model classes have to implement this method, if such a
  transformation exists.
docstr_tok: ["This","function","can","be","used","to","prepare","the","architecture","for","inference",".","For","example",",","re-parameterizing","branches","when","possible",".","The","functionality","of","this","method","may","vary","from","model","to","model",",","so","child","model","classes","have","to","implement","this","method",",","if","such","a","transformation","exists","."]
code: def get_exportable_model(self) -> nn.Module:
      """
      This function can be used to prepare the architecture for inference. For example,
      re-parameterizing branches when possible. The functionality of this method may vary
      from model to model, so child model classes have to implement this method, if such a
      transformation exists.
      """
      return self
  
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 13 for calling the BaseImageEncoder obj's underlying member method code with example usage: obj.get_exportable_model() and returns: self