code: def forward(self, input):
      s = input.shape[0]
      assert s % self.num_experts == 0, f"{s} % {self.num_experts} != 0"
      capacity = 2 * s // self.num_experts
      output = torch.zeros(
          s,
          self.num_experts,
          capacity,
          dtype=input.dtype,
          device=input.device,
      )
      for i in range(s):
          output[i, i % self.num_experts, i // self.num_experts] = 1.0
      return 0.0, output, output.bool()
  
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 2 for calling the RoundRobinGate obj's underlying member method code with example usage: obj.forward(input) and returns: int,  output,  output