code: def forward(self, x):
      l1_out = torch.split(x.to(self.device), 10, dim=2)
      post_l1 = [
          torch.nn.functional.layer_norm(
              l1_out[i],
              (10,),
              weight=self.scale0[i],
              bias=self.bias0[i],
          )
          for i in range(len(l1_out))
      ]
      l1_out = torch.cat(post_l1, dim=2)
  
      l2_out = torch.split(l1_out, 10, dim=2)
      post_l2 = [
          torch.nn.functional.layer_norm(
              l2_out[i],
              (5, 10),
              weight=self.scale1[i],
              bias=self.bias1[i],
          )
          for i in range(len(l2_out))
      ]
  
      return torch.cat(post_l2, dim=2)
  
imports: import functools
  import unittest
  import torch
  import torch._inductor
  from torch._dynamo.test_case import run_tests, TestCase
  from torch._dynamo.utils import counters
  from torch.testing._internal.inductor_utils import HAS_CUDA
label_desc: Use image node_id 2 for calling the MyModule3 obj's underlying member method code with example usage: obj.forward(x) and returns: torch