class_docstr: Handles partitioning and gathering of parameters.
code: def __params_to_release(
      self, submodule_to_release: Module, step_id: int
  ) -> Set[int]:
      if not self.is_complete_trace():
          raise RuntimeError("expected trace to be complete")
  
      params_to_release = set(
          p.ds_id
          for p in iter_params(submodule_to_release)
          if not p.ds_persist
      )
  
      # Problem: When prefetcher scans the param trace, it skips AVAILABLE params.
      # This creates issues if those params are released before the skipped uses:
      # 1) It hurts performance as the skipped uses are never prefetched.
      # 2) For nvme params, we run out of swap buffers because the prefetch order
      # diverges from the trace.
      # Solution: Don't release params whose reuse was skipped by prefetch. This is
      # possible because we detect such skips during prefetch and mark those params.
      for param in iter_params(submodule_to_release):
          if (
              self.__most_recent_step_id_param_fetched_for[param]
              > step_id
          ):
              params_to_release.discard(param.ds_id)
  
      # examine all modules within `max_reuse_dist_in_numel` of the current step,
      # if we see any of the candidate parameters to be released reoccur while
      # doing this, remove them from the set of parameters to release.
      params_traversed = 0
      for module in self.__submodule_order[step_id:]:
          if params_traversed >= self.__max_reuse_dist_in_numel:
              break
          for param in iter_params(module):
              params_to_release.discard(param.ds_id)
              params_traversed += param.ds_numel
  
      return params_to_release
  
imports: from dataclasses import dataclass
  import collections
  from collections import UserDict
  from typing import Deque, Set
  from torch.cuda import Event, Stream
  from deepspeed import comm
  from deepspeed.utils.logging import logger
  from deepspeed.runtime.zero.offload_config import OffloadDeviceEnum
  from deepspeed.runtime.zero.partition_parameters import *
  from deepspeed.runtime.swap_tensor.partitioned_param_swapper import PartitionedParamStatus
  from deepspeed.utils.debug import debug_module2name_id, debug_param2name_id
label_desc: Use image node_id 19 for calling the PartitionedParameterCoordinator obj's underlying member method code with example usage: obj.__params_to_release(submodule_to_release, step_id) and returns: params_to_release