class_docstr: Base class for any neural network
class_docstr_tok: ["Base","class","for","any","neural","network"]
docstr: Get parameters for training along with the learning rate.
  
  Args:
      weight_decay: weight decay
      no_decay_bn_filter_bias: Do not decay BN and biases. Defaults to False.
  
  Returns:
       Returns a tuple of length 2. The first entry is a list of dictionary with three keys
       (params, weight_decay, param_names). The second entry is a list of floats containing
       learning rate for each parameter.
  
  Note:
      Kwargs may contain module_name. To avoid multiple arguments with the same name,
      we pop it and concatenate with encoder or head name
docstr_tok: ["Get","parameters","for","training","along","with","the","learning","rate",".","Args",":","weight_decay",":","weight","decay","no_decay_bn_filter_bias",":","Do","not","decay","BN","and","biases",".","Defaults","to","False",".","Returns",":","Returns","a","tuple","of","length","2",".","The","first","entry","is","a","list","of","dictionary","with","three","keys","(","params",",","weight_decay",",","param_names",")",".","The","second","entry","is","a","list","of","floats","containing","learning","rate","for","each","parameter",".","Note",":","Kwargs","may","contain","module_name",".","To","avoid","multiple","arguments","with","the","same","name",",","we","pop","it","and","concatenate","with","encoder","or","head","name"]
code: def get_trainable_parameters(
      self,
      weight_decay: float = 0.0,
      no_decay_bn_filter_bias: bool = False,
      module_name: str = "",
      *args,
      **kwargs,
  ) -> Tuple[List[Mapping], List[float]]:
      """Get parameters for training along with the learning rate.
  
      Args:
          weight_decay: weight decay
          no_decay_bn_filter_bias: Do not decay BN and biases. Defaults to False.
  
      Returns:
           Returns a tuple of length 2. The first entry is a list of dictionary with three keys
           (params, weight_decay, param_names). The second entry is a list of floats containing
           learning rate for each parameter.
  
      Note:
          Kwargs may contain module_name. To avoid multiple arguments with the same name,
          we pop it and concatenate with encoder or head name
      """
      param_list = parameter_list(
          named_parameters=self.named_parameters,
          weight_decay=weight_decay,
          no_decay_bn_filter_bias=no_decay_bn_filter_bias,
          module_name=module_name,
          *args,
          **kwargs,
      )
      return param_list, [1.0] * len(param_list)
  
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 6 for calling the BaseAnyNNModel obj's underlying member method code with example usage: obj.get_trainable_parameters(weight_decay, no_decay_bn_filter_bias, module_name) and returns: param_list,  unknown