code: def fast_decode(data, critial_data, is_postscore=True):
      assert (
          data.is_contiguous()
      ), "Input tensor for encode/decode should be in contiguous memory format."
      num_global_experts = critial_data[0]
      dispatcher = TutelMoeFastDispatcher(
          num_global_experts, 0, data.size(-1), data.dtype
      )
      dispatcher.update(*critial_data[1:-1], is_postscore=is_postscore)
      return dispatcher.decode(data).view(-1, data.size(-1))
  
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 5 for calling a global function with example usage: fast_decode(data, critial_data, is_postscore) and returns: dispatcher