code: def get_optimizer_parameters(self, config):
      optimizer_param_groups = []
  
      base_lr = config.optimizer.params.lr
      # collect all the parameters that need different/scaled lr
      finetune_params_set = set()
      for m in self.finetune_modules:
          optimizer_param_groups.append(
              {
                  "params": list(m["module"].parameters()),
                  "lr": base_lr * m["lr_scale"],
              }
          )
          finetune_params_set.update(list(m["module"].parameters()))
      # remaining_params are those parameters w/ default lr
      remaining_params = [
          p for p in self.parameters() if p not in finetune_params_set
      ]
      # put the default lr parameters at the beginning
      # so that the printed lr (of group 0) matches the default lr
      optimizer_param_groups.insert(0, {"params": remaining_params})
  
      return optimizer_param_groups
  
imports: import functools
  import logging
  import math
  import torch
  import torch.nn.functional
  from mmf.common.registry import registry
  from mmf.models.base_model import BaseModel
  from mmf.modules.layers import ClassifierLayer
  from mmf.utils.build import build_image_encoder
  from omegaconf import OmegaConf
  from torch import nn
label_desc: Use image node_id 17 for calling the M4C obj's underlying member method code with example usage: obj.get_optimizer_parameters(config) and returns: optimizer_param_groups