class_docstr: Base class for any neural network
class_docstr_tok: ["Base","class","for","any","neural","network"]
docstr: This function can be used to adjust the learning rate of each layer in a model. The
  functionality of this function may vary from model to model, so we do not implement
  it in the base class and expects child model classes will implement this function, if desired.
docstr_tok: ["This","function","can","be","used","to","adjust","the","learning","rate","of","each","layer","in","a","model",".","The","functionality","of","this","function","may","vary","from","model","to","model",",","so","we","do","not","implement","it","in","the","base","class","and","expects","child","model","classes","will","implement","this","function",",","if","desired","."]
code: def _apply_layer_wise_lr(
      self,
      weight_decay: Optional[float] = 0.0,
      no_decay_bn_filter_bias: Optional[bool] = False,
      *args,
      **kwargs,
  ):
      """This function can be used to adjust the learning rate of each layer in a model. The
      functionality of this function may vary from model to model, so we do not implement
      it in the base class and expects child model classes will implement this function, if desired.
      """
      raise NotImplementedError(
          f"Please implement _apply_layer_wise_lr function for {self.__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 5 for calling the BaseAnyNNModel obj's underlying member method code with example usage: obj._apply_layer_wise_lr(weight_decay, no_decay_bn_filter_bias) without return types