code: def forward(self, inp):
      if self.mask.device != inp.device:
          self.mask = self.mask.to(inp.device)
  
      gate_score = self.gate(inp)
      lim_mask = self.mask
  
      top2_val, top2_idx = torch.topk(gate_score, k=2, dim=-1)
      S = gate_score.shape[0]
      top_k = 2
  
      with torch.no_grad():
          top1_idx = top2_idx.view((-1, top_k))[:, 0]
          top1_val = top2_val.view((-1, top_k))[:, 0]
      c_e = (
          torch.scatter_add(
              torch.zeros(self.tot_expert, device=top1_idx.device),
              0,
              top1_idx,
              torch.ones_like(top1_idx, dtype=torch.float),
          )
          / S
      )
      m_e = torch.mean(F.softmax(gate_score, dim=1), dim=0)
      loss = torch.mean(c_e * m_e) * (self.num_expert**2)
      self.set_loss(loss)
  
      with torch.no_grad():
          if self.policy_fn is None:
              stored_models = torch.zeros(
                  self.num_expert * self.world_size, dtype=torch.bool
              )
          else:
              # TODO: Fix this after expert shadowing is ported
              _, lec, aec, gec, agec = count_by_gate(
                  top2_idx,
                  self.num_expert,
                  self.world_size,
                  require_pos=False,
              )
              stored_models = self.policy_fn(
                  aec,
                  agec,
                  self.num_expert,
                  self.world_size,
                  inp.shape[-1],
                  True,
              )
          lim_mask = lim_mask & ~stored_models.view(-1).to(
              lim_mask.device
          )
  
          ogn_mask = lim_mask[top1_idx]
          ogn_thres = int(inp.shape[0] * self.ogn_ratio)
  
      if ogn_mask.sum().item() < ogn_thres:
          topk_val, topk_idx = torch.topk(gate_score, k=self.top_k)
          topk_val = F.softmax(topk_val, dim=-1)
          return topk_idx, topk_val
  
      with torch.no_grad():
          top1_val[~ogn_mask] = float("-inf")
          _, top_ogn = torch.topk(top1_val.view(-1), k=ogn_thres)
          cand = gate_score.clone()
          cand[:, lim_mask] = float("-inf")
          _, topk_idx = torch.topk(cand, k=self.top_k)
          topk_idx[top_ogn, 1] = top1_idx.view(-1)[top_ogn]
  
      idx_x = torch.arange(
          inp.shape[0], device=inp.device
      ).repeat_interleave(2)
      topk_val = gate_score[idx_x, topk_idx.view(-1)].view(
          -1, self.top_k
      )
  
      topk_val = F.softmax(topk_val, dim=-1)
  
      return topk_idx, topk_val
  
file_path: smart-moe/fmoe/gates/faster_gate.py
imports: from .naive_gate import NaiveGate
  import os
  import sys
  import torch
  import torch.nn.functional
  from .utils import limit_by_capacity
  import fmoe_cuda
  from fmoe.functions import count_by_gate
label_desc: Use image node_id 2 for calling the FasterGate obj's underlying member method code with example usage: obj.forward(inp) and returns: topk_idx,  topk_val, topk_idx,  topk_val