class_docstr: Base class for any neural network
class_docstr_tok: ["Base","class","for","any","neural","network"]
docstr: Build a model from command-line arguments. Sub-classes must implement this method
  
  Args:
      opts: Command-line arguments
  
  ...note::
      This function is typically implemented in the base class for each task and
      implementation is reused by all models in that task.
docstr_tok: ["Build","a","model","from","command-line","arguments",".","Sub-classes","must","implement","this","method","Args",":","opts",":","Command-line","arguments","...","note",":",":","This","function","is","typically","implemented","in","the","base","class","for","each","task","and","implementation","is","reused","by","all","models","in","that","task","."]
code: def build_model(
      cls, opts: argparse.Namespace, *args, **kwargs
  ) -> BaseAnyNNModel:
      """Build a model from command-line arguments. Sub-classes must implement this method
  
      Args:
          opts: Command-line arguments
  
      ...note::
          This function is typically implemented in the base class for each task and
          implementation is reused by all models in that task.
      """
      raise NotImplementedError(
          f"Please implement build_model function for {cls.__name__}"
      )
  
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 10 for calling the BaseAnyNNModel obj's underlying member method code with example usage: obj.build_model(cls, opts) without return types