code: def test_forward_backward(self, model_name):
      model = models.get_model(
          model_name, **self.model_defaults
      ).train()
      train_return_nodes, eval_return_nodes = self._get_return_nodes(
          model
      )
      model = self._create_feature_extractor(
          model,
          train_return_nodes=train_return_nodes,
          eval_return_nodes=eval_return_nodes,
      )
      out = model(self.inp)
      out_agg = 0
      for node_out in out.values():
          if isinstance(node_out, Sequence):
              out_agg += sum(
                  o.float().mean() for o in node_out if o is not None
              )
          elif isinstance(node_out, Mapping):
              out_agg += sum(
                  o.float().mean()
                  for o in node_out.values()
                  if o is not None
              )
          else:
              # Assume that the only other alternative at this point is a Tensor
              out_agg += node_out.float().mean()
      out_agg.backward()
  
imports: import random
  from itertools import chain
  from typing import Mapping, Sequence
  import pytest
  import torch
  from common_utils import set_rng_seed
  from torchvision import models
  from torchvision.models._utils import IntermediateLayerGetter
  from torchvision.models.detection.backbone_utils import BackboneWithFPN, mobilenet_backbone, resnet_fpn_backbone
  from torchvision.models.feature_extraction import create_feature_extractor, get_graph_node_names
label_desc: Use image node_id 5 for calling the TestFxFeatureExtraction obj's underlying member method code with example usage: obj.test_forward_backward(model_name) without return types