class_docstr: Base class for any neural network
class_docstr_tok: ["Base","class","for","any","neural","network"]
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: from __future__ import annotations
  import argparse
  import traceback
  from typing import Any, Dict, List, Mapping, Optional, Tuple
  import torch
  from torch import nn
  from cvnets.layers import norm_layers_tuple
  from cvnets.misc.common import get_tensor_sizes, parameter_list
  from cvnets.misc.init_utils import initialize_weights
  from options.parse_args import JsonValidator
  from utils import logger
  from utils.common_utils import check_frozen_norm_layer
  from utils.ddp_utils import is_master
label_desc: Use image node_id 8 for calling the BaseAnyNNModel obj's underlying member method code with example usage: obj.get_exportable_model() and returns: self