code: def get_optimizer_parameters(self, config):
      detr_params = [
          {
              "params": [
                  p
                  for n, p in self.unit_base_model.named_parameters()
                  if "backbone" not in n and p.requires_grad
              ]
          },
          {
              "params": [
                  p
                  for n, p in self.unit_base_model.named_parameters()
                  if "backbone" in n and p.requires_grad
              ],
              "lr": self.config.base_args.lr_backbone,
          },
      ]
  
      vqa_params = [
          {"params": self.bert_model.parameters()},
          {"params": self.bert_projection.parameters()},
          {"params": self.task_embeddings_lang.parameters()},
          {"params": self.bert_pos_projection.parameters()},
          {"params": self.classifiers.parameters()},
          {"params": self.class_embeds.parameters()},
          {"params": self.bbox_embeds.parameters()},
          {"params": self.det_losses.parameters()},
      ]
      return vqa_params + detr_params
  
imports: import logging
  from copy import deepcopy
  from typing import Dict
  import torch
  from mmf.common.registry import registry
  from mmf.models import BaseModel
  from mmf.models.unit.unit_base_model import AttributeHead, build_detection_loss, MLP, UniTBaseModel
  from mmf.modules.encoders import TransformerEncoder
  from mmf.utils.distributed import byte_tensor_to_object
  from torch import nn, Tensor
label_desc: Use image node_id 9 for calling the UniT obj's underlying member method code with example usage: obj.get_optimizer_parameters(config) and returns: unknown