code: def forward(ctx: Any, config: Any, reshaped_input: Tensor, *gates_):
      ctx.reshaped_input = reshaped_input
      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_
          )
  
      dispatched_input = torch.zeros(
          [
              ctx.config.num_global_experts * ctx.config.capacity,
              ctx.config.model_dim,
          ],
          dtype=reshaped_input.dtype,
          device=reshaped_input.device,
      )
      for g, i, l in zip(
          ctx.gates_h2, ctx.config.indices_, ctx.config.locations_
      ):
          ctx.config.func_fwd(
              g,
              i,
              l,
              reshaped_input,
              dispatched_input,
              extra=[
                  ctx.config.indices_[0].size(0),
                  ctx.config.aligned_dim,
                  ctx.config.capacity,
              ],
          )
      return dispatched_input
  
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 GatingEncoder obj's underlying member method code with example usage: obj.forward(ctx, config, reshaped_input) and returns: dispatched_input