class_docstr: This contains the data structures to track the execution order. We track
  the pre-forward order on the *first* iteration for forward prefetching
  (which thus assumes static graph) and the post-forward order on *every*
  iteration for backward prefetching (which thus does not assume static
  graph but may be provide an incorrect order).
docstr: Returns a :class:`list` of the handles keys of the handles to forward
  prefetch given the current handles key. If there are no valid handles
  keys to prefetch, then this returns an empty :class:`list`.
docstr_tok: ["Returns","a",":","class",":","`","list","`","of","the","handles","keys","of","the","handles","to","forward","prefetch","given","the","current","handles","key",".","If","there","are","no","valid","handles","keys","to","prefetch",",","then","this","returns","an","empty",":","class",":","`","list","`","."]
code: def get_handle_to_forward_prefetch(
      self,
      current_handle: FlatParamHandle,
  ) -> Optional[FlatParamHandle]:
      """
      Returns a :class:`list` of the handles keys of the handles to forward
      prefetch given the current handles key. If there are no valid handles
      keys to prefetch, then this returns an empty :class:`list`.
      """
      current_index = current_handle._pre_forward_order_index
      if current_index is None:
          return None
      target_index = current_index + 1
      target_handle: Optional[FlatParamHandle] = None
      for _ in range(self._forward_prefetch_limit):
          if target_index >= len(self.handles_pre_forward_order):
              break
          target_handle = self.handles_pre_forward_order[target_index]
          target_index += 1
      return target_handle
  
imports: import itertools
  import warnings
  from enum import auto, Enum
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  import torch.distributed
  import torch.distributed.fsdp._traversal_utils
  import torch.nn
  from torch.distributed.fsdp._common_utils import _FSDPState, _get_param_to_fqns
  from torch.distributed.fsdp._flat_param import FlatParamHandle
label_desc: Use image node_id 5 for calling the _ExecOrderData obj's underlying member method code with example usage: obj.get_handle_to_forward_prefetch(current_handle) and returns: target_handle, None