code: def forward(self, x):
      x = x + 1.2
      x1 = [
          torch.nn.functional.linear(
              x,
              self.weights1[i],
              self.biases1[i] if self.has_bias else None,
          )
          for i in range(self.seq_len)
      ]
      x2 = torch.cat(x1, dim=1)
      x3 = torch.split(x2, 10, dim=1)
      x4 = torch.cat(x3)
      x5 = [
          torch.nn.functional.linear(
              x4,
              self.weights2[i],
              self.biases2[i] if self.has_bias else None,
          )
          for i in range(self.seq_len)
      ]
      x6 = torch.cat(x5, dim=1)
      return torch.sigmoid(x6)
  
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 MyModule4 obj's underlying member method code with example usage: obj.forward(x) and returns: torch