code: def forward(self, x):
      inputs = torch.split(x.to(self.device), 500, dim=1)
      x_split = torch.split(inputs[0].to(self.device), 50, dim=1)
      y_split = torch.split(inputs[1].to(self.device), 50, dim=1)
      tanh_1 = [torch.tanh(x_split[i]) for i in range(len(x_split))]
      tanh_2 = [torch.tanh(y_split[i]) for i in range(len(y_split))]
      sigmoid_1 = [torch.sigmoid(tanh_1[i]) for i in range(len(tanh_1))]
      sigmoid_2 = [torch.sigmoid(tanh_2[i]) for i in range(len(tanh_2))]
      relu_1 = [
          torch.nn.functional.relu(sigmoid_1[i])
          for i in range(len(sigmoid_1))
      ]
      relu_2 = [
          torch.nn.functional.relu(sigmoid_2[i])
          for i in range(len(sigmoid_2))
      ]
      add = [
          torch.add(relu_1[i], relu_2[i]) for i in range(len(relu_1))
      ]
      mul = [torch.mul(add[i], add[i]) for i in range(len(add))]
      sub = [torch.sub(mul[i], mul[i]) for i in range(len(mul))]
      div = [torch.div(sub[i], sub[i]) for i in range(len(sub))]
      return torch.cat(div, dim=1)
  
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 TestPoitwiseOps obj's underlying member method code with example usage: obj.forward(x) and returns: torch