code: def forward(self, inp):
      outs = [self.models[i](inp)[0] for i in range(self.top_k)]
      out = sum(outs) / self.top_k
      return (
          out,
          torch.zeros(
              inp.shape[-1], dtype=inp.dtype, device=inp.device
          ),
      )
  
imports: import math
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from fmoe.transformer import FMoETransformerMLP
  from fmoe.utils import get_torch_default_comm
  from .balance import reset_gate_hook
  from .balance import generate_megatron_gate_hook
  from .distributed import set_moe_group
label_desc: Use image node_id 2 for calling the BaseMLP obj's underlying member method code with example usage: obj.forward(inp) and returns: out,  torch