class_docstr: Base class for `neural (or range) augmentation <https://arxiv.org/abs/2212.10553>`_
class_docstr_tok: ["Base","class","for","`","neural","(","or","range",")","augmentation","<","https",":","\/\/arxiv.org\/abs\/2212.10553",">","`","_"]
docstr: Get trainable parameters
docstr_tok: ["Get","trainable","parameters"]
code: def get_trainable_parameters(
      self,
      weight_decay: Optional[float] = 0.0,
      no_decay_bn_filter_bias: Optional[bool] = False,
      *args,
      **kwargs,
  ):
      """Get trainable parameters"""
      param_list = parameter_list(
          named_parameters=self.named_parameters,
          weight_decay=weight_decay,
          no_decay_bn_filter_bias=no_decay_bn_filter_bias,
      )
      return param_list, [self.lr_multiplier] * len(param_list)
  
imports: import argparse
  import random
  from typing import List, Optional
  import torch
  from torch import Tensor, nn
  from cvnets.misc.common import parameter_list
  from cvnets.neural_augmentor.utils.neural_aug_utils import Clip, FixedSampler, UniformSampler, random_brightness, random_contrast, random_noise
  from utils import logger
label_desc: Use image node_id 3 for calling the BaseNeuralAugmentor obj's underlying member method code with example usage: obj.get_trainable_parameters(weight_decay, no_decay_bn_filter_bias) and returns: param_list,  unknown