code: def forward(ctx: Any, config: Any, expert_output: Tensor, *gates_):
      ctx.expert_output = expert_output
      ctx.config = config
      if gates_:
          ctx.gates_h2 = [
              x.view(-1, 1).repeat(1, 2)
              if x.dtype == torch.float16
              else x
              for x in gates_
          ]
      else:
          ctx.gates_h2 = [ctx.config.ones_helper] * len(
              ctx.config.indices_
          )
  
      last_result = None
      for g, i, l in zip(
          ctx.gates_h2, ctx.config.indices_, ctx.config.locations_
      ):
          single_output = torch.empty(
              [config.sample_size, config.model_dim],
              dtype=expert_output.dtype,
              device=expert_output.device,
          )
          config.func_bwd_data(
              g,
              i,
              l,
              single_output,
              expert_output,
              extra=[
                  ctx.config.indices_[0].size(0),
                  ctx.config.aligned_dim,
                  ctx.config.capacity,
              ],
          )
          last_result = (
              single_output
              if last_result is None
              else last_result + single_output
          )
      return last_result
  
imports: from typing import TYPE_CHECKING, Any, Optional, Tuple, Union, cast
  import logging
  import torch
  from torch import Tensor
  from .jit_compiler import IS_HIP_EXTENSION
  from ..jit_kernels import sparse
  from ..jit_kernels.gating import fast_cumsum_sub_one
  from .communicate import get_world_rank, simple_all_reduce
  from .None import losses
label_desc: Use image node_id 1 for calling the GatingDecoder obj's underlying member method code with example usage: obj.forward(ctx, config, expert_output) and returns: last_result