code: def test_issue106555(self):
      DEVICE = torch.device("cuda:0")
      NUM_FEATURES = 256
  
      def bias_sigmoid_mul(x1, x2, bias):
          x2 = torch.sigmoid(x2 + bias)
          y = x1 * x2
          return y
  
      bias_sigmoid_mul_jit = torch.compile(bias_sigmoid_mul)
  
      class ModuleWithJit(nn.Module):
          def __init__(self):
              super().__init__()
              self.linear_1 = nn.Linear(
                  NUM_FEATURES, NUM_FEATURES, bias=True
              )
              self.linear_2 = nn.Linear(
                  NUM_FEATURES, NUM_FEATURES, bias=False
              )
              self.linear_2_bias = nn.Parameter(
                  torch.zeros(NUM_FEATURES)
              )
  
          def forward(self, input_tensor):
              x1 = self.linear_1(input_tensor)
              x2 = self.linear_2(input_tensor)
              output = bias_sigmoid_mul_jit(x1, x2, self.linear_2_bias)
              return output
  
      class Model(nn.Module):
          def __init__(self):
              super().__init__()
              self.module_with_jit_1 = ModuleWithJit()
              self.module_with_jit_2 = ModuleWithJit()
  
          def forward(self, x, gradient_checkpointing: bool):
              if gradient_checkpointing:
                  y = torch.utils.checkpoint.checkpoint(
                      self._forward, x, use_reentrant=True
                  )
              else:
                  y = self._forward(x)
              return y
  
          def _forward(self, x):
              x = x + self.module_with_jit_1(x)
              x = x + self.module_with_jit_2(
                  x.transpose(-2, -3)
              ).transpose(-2, -3)
              return x
  
      torch.cuda.set_device(device=DEVICE)
      torch.manual_seed(1234567890)
      model = Model()
      model.train()
      model.to(device=DEVICE)
      model_parameters = list(model.parameters())
  
      torch.manual_seed(1234567890)
      input_tensor = torch.randn(1, 128, 256, NUM_FEATURES).to(
          device=DEVICE
      )
      input_tensor.requires_grad = True
      target_tensor = torch.randn(1, 128, 256, NUM_FEATURES).to(
          dtype=input_tensor.dtype, device=DEVICE
      )
  
      for iteration in range(10):
          for param in model_parameters:
              param.grad = None
          output_tensor = model(
              x=input_tensor.clone(),
              gradient_checkpointing=True,
          )
          loss = torch.mean(torch.abs(target_tensor - output_tensor))
          loss.backward()
  
imports: import functools
  import re
  import sys
  import unittest
  from importlib.machinery import SourceFileLoader
  from pathlib import Path
  from unittest import mock
  import torch
  import torch.nn
  from torch import _inductor
  from torch._dynamo import compiled_autograd
  from torch._dynamo.test_case import run_tests, TestCase
  from torch._dynamo.utils import counters
  from torch.testing._internal.inductor_utils import HAS_CPU, HAS_CUDA
label_desc: Use image node_id 13 for calling the TestCompiledAutograd obj's underlying member method code with example usage: obj.test_issue106555() and returns: y, output, y, x