code: def get_optimizer_parameters(self, config):
      lr = config.optimizer.params.lr
  
      param_list = []
      parameters = []
      head_configs = self.config.get("heads", [])
      for name, module in self.named_children():
          # Heads can have different learning rates. This is handled here
          if name == "heads":
              # Parameters in the head which have a separate learning
              # rate, are added as a separate param group
              for head_config, head in zip(head_configs, self.heads):
                  parameters, param_list = self.set_lr_for_parameters(
                      config=head_config,
                      module_name="{} head".format(
                          head_config.get("type", "MLP")
                      ),
                      base_lr=lr,
                      module=head,
                      parameters=parameters,
                      param_list=param_list,
                  )
          elif name == "encoders":
              for key in module:
                  for modality in self.config.modalities:
                      if key == modality.key:
                          modality_config = modality
                  parameters, param_list = self.set_lr_for_parameters(
                      config=modality_config,
                      module_name=f"{key} encoder",
                      base_lr=lr,
                      module=module[key],
                      parameters=parameters,
                      param_list=param_list,
                  )
          else:
              # For other modules in trunk, add to same param group
              param_list += list(module.named_parameters())
  
      parameters += get_bert_configured_parameters(param_list)
  
      return parameters
  
imports: import logging
  from abc import ABC, abstractmethod
  from dataclasses import asdict, dataclass, field
  from typing import Any, Dict, List, NamedTuple, Optional, Tuple, Type
  from mmf.common.registry import registry
  from mmf.models import BaseModel
  from mmf.modules.encoders import IdentityEncoder
  from mmf.utils.modeling import get_bert_configured_parameters
  from omegaconf import MISSING, OmegaConf
  from torch import nn, Tensor
label_desc: Use image node_id 3 for calling the BaseTransformer obj's underlying member method code with example usage: obj.get_optimizer_parameters(config) and returns: parameters