code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      x1 = [x + 0.1 * i for i in range(self.seq_len)]
      x2 = [self.seq1[i](x1[i]) for i in range(self.seq_len)]
      x3 = [x2[i] - 0.1 * i for i in range(self.seq_len)]
      x4 = [x1[i] for i in range(3)] + [
          x3[i] for i in range(3, self.seq_len)
      ]
      x5 = [self.seq2[i](x4[i]) for i in range(self.seq_len)]
      x6 = [
          x5[i] + 0.1 * (self.seq_len - i) for i in range(self.seq_len)
      ]
      x7 = (
          [x1[i] for i in range(4)]
          + [x3[i] for i in range(6, 8)]
          + [x6[i] for i in range(4)]
      )
      x8 = [self.seq3[i](x7[i]) for i in range(self.seq_len)]
      x9 = torch.cat(x8, dim=1)
      return x9
  
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 MyModule obj's underlying member method code with example usage: obj.forward(x) and returns: x9