code: def test_module_partitioner_functional_linear_relu_linear(self):
      class M(torch.nn.Module):
          def __init__(self):
              super().__init__()
  
          def forward(self, x, weight, bias):
              x = torch.nn.functional.linear(x, weight, bias)
              x = torch.nn.functional.linear(x, weight, bias)
              x = torch.nn.functional.relu(x)
              x = torch.nn.functional.linear(x, weight, bias)
              x = torch.nn.functional.linear(x, weight, bias)
              x = torch.nn.functional.relu(x)
              return x
  
      inputs = (torch.randn(1, 5), torch.rand((5, 5)), torch.zeros(5))
      gm, _ = torch._dynamo.export(M(), aten_graph=True)(*inputs)
      gm.graph.eliminate_dead_code()
  
      module_partitions = get_source_partitions(
          gm.graph,
          [torch.nn.functional.linear, torch.nn.functional.relu],
      )
  
      self.assertEqual(len(module_partitions), 2)
      self.assertEqual(
          len(module_partitions[torch.nn.functional.linear]), 4
      )
      self.assertEqual(
          len(module_partitions[torch.nn.functional.relu]), 2
      )
  
file_path: pytorch/test/fx/test_source_matcher_utils.py
imports: import os
  import sys
  import unittest
  import torch
  from torch._dynamo.eval_frame import is_dynamo_supported
  from torch.fx.passes.utils.source_matcher_utils import get_source_partitions, check_subgraphs_connected
  from torch.testing._internal.jit_utils import JitTestCase
label_desc: Use image node_id 4 for calling the TestSourceMatcher obj's underlying member method code with example usage: obj.test_module_partitioner_functional_linear_relu_linear() and returns: x