class_docstr: Base class for different image classification models
class_docstr_tok: ["Base","class","for","different","image","classification","models"]
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.
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","."]
code: def get_trainable_parameters(
      self,
      weight_decay: Optional[float] = 0.0,
      no_decay_bn_filter_bias: Optional[bool] = False,
      *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.
      """
      if self.enable_layer_wise_lr_decay:
          return self._apply_layer_wise_lr(
              weight_decay=weight_decay,
              no_decay_bn_filter_bias=no_decay_bn_filter_bias,
              *args,
              **kwargs,
          )
      return super().get_trainable_parameters(
          weight_decay=weight_decay,
          no_decay_bn_filter_bias=no_decay_bn_filter_bias,
          *args,
          **kwargs,
      )
  
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 11 for calling the BaseImageEncoder obj's underlying member method code with example usage: obj.get_trainable_parameters(weight_decay, no_decay_bn_filter_bias) and returns: super, self