code: def test_addmm_broadcasting_bias(self):
      class Model(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.linear = torch.nn.functional.linear
              self.linear_weight = torch.randn(4, 4).cuda()
              self.bias = torch.randn(1, 4).cuda()
  
          def forward(self, x):
              x = self.linear(x, self.linear_weight, self.bias)
              return x
  
      input_tensor = torch.randn(1, 3, 4).cuda()
  
      func = Model().cuda()
  
      res1 = func(input_tensor)
      jit_func = torch.compile(func)
      res2 = jit_func(input_tensor)
  
      self.assertEqual(res1, res2)
  
imports: import copy
  import unittest
  import torch
  import torch._dynamo.config
  import torch._inductor.config
  from torch._dynamo.test_case import run_tests, TestCase
  from torch._dynamo.utils import count_calls, counters
  from torch._higher_order_ops.out_dtype import out_dtype
  from torch._inductor.fx_passes import joint_graph
  from torch._inductor.fx_passes.serialized_patterns.central_index import get_serialized_pattern
  from torch._inductor.pattern_matcher import _TargetExpr, Arg, CallFunction, gen_pattern, KeywordArg, Match, PatternExpr, PatternMatcherPass, PatternPrettyPrinter, register_graph_pattern, stable_topological_sort
  from torch._inductor.utils import run_and_get_code
  from torch._inductor.virtualized import V
  from torch.testing import FileCheck
  from torch.testing._internal.common_cuda import SM80OrLater
  from torch.testing._internal.common_utils import IS_LINUX, skipIfRocm
  from torch.testing._internal.inductor_utils import HAS_CUDA
label_desc: Use image node_id 18 for calling the TestPatternMatcher obj's underlying member method code with example usage: obj.test_addmm_broadcasting_bias() and returns: x