code: def forward(device):
      make_cudnn_deterministic()
      model_dim = 8
      num_experts = dist.get_world_size(dist.group.WORLD)
      input = torch.randn(4, 16, model_dim).to(device)
      gate = Top2Gate(model_dim, num_experts)
      expert = torch.nn.Linear(model_dim, model_dim, bias=False)
      # Use identity matrix
      expert.weight = torch.nn.Parameter(torch.eye(model_dim))
      moe = MOELayer(gate, expert).to(device)
      output = moe(input)
      assert (
          output.shape == input.shape
      ), f"{output.shape} != {input.shape}"
      # Re-assembled output should match input due to identity expert.
      torch.testing.assert_allclose(input, output)
  
imports: import functools
  import tempfile
  import pytest
  import torch
  import torch.distributed
  import torch.multiprocessing
  from fairscale.fair_dev.testing.testing import make_cudnn_deterministic
  from fairscale.internal import torch_version
  from fairscale.nn import MOELayer, Top2Gate
label_desc: Use image node_id 5 for calling a global function with example usage: forward(device) without return types