code: def test_leaf_module_and_function(self):
      class LeafModule(torch.nn.Module):
          def forward(self, x):
              # This would raise a TypeError if it were not in a leaf module
              int(x.shape[0])
              return torch.nn.functional.relu(x + 4)
  
      class TestModule(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.conv = torch.nn.Conv2d(3, 1, 3)
              self.leaf_module = LeafModule()
  
          def forward(self, x):
              leaf_function(x.shape[0])
              x = self.conv(x)
              return self.leaf_module(x)
  
      model = self._create_feature_extractor(
          TestModule(),
          return_nodes=["leaf_module"],
          tracer_kwargs={
              "leaf_modules": [LeafModule],
              "autowrap_functions": [leaf_function],
          },
      ).train()
  
      # Check that LeafModule is not in the list of nodes
      assert "relu" not in [str(n) for n in model.graph.nodes]
      assert "leaf_module" in [str(n) for n in model.graph.nodes]
  
      # Check forward
      out = model(self.inp)
      # And backward
      out["leaf_module"].float().mean().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 9 for calling the TestFxFeatureExtraction obj's underlying member method code with example usage: obj.test_leaf_module_and_function() and returns: torch, self