class_docstr: Handles partitioning and gathering of parameters.
code: def __init__(
      self,
      prefetch_bucket_sz: int,
      max_reuse_distance_in_numel: int,
      max_available_parameters_in_numel: int,
      allgather_stream: Stream,
      prefetch_nvme: bool = False,
  ) -> None:
      # mapping of param -> handle for each param that is currently in flight
      self.__inflight_param_registry = (
          __class__.__InflightParamRegistry()
      )
      # keeps track of the number of submodules invoked so far.
      self.__step_id: int = 0
      # network tracing mode
      self.__trace_mode: ZeRoTraceMode = ZeRoTraceMode.RECORD
      # sequence of submodules/parameters in forward pass + backward pass
      self.__submodule_order: Iterable[Module] = []
      self.__param_order: Iterable[__class__.__ParamInTrace] = []
      self.__most_recent_step_id_param_fetched_for = (
          collections.defaultdict(lambda: int(-1e10))
      )
      self.__step_id_module_fetched_for = collections.defaultdict(
          lambda: collections.deque()
      )
      # number of available params, and max number of available params
      self.__n_available_params: int = 0
      self.__max_n_available_params: int = (
          max_available_parameters_in_numel
      )
      # max distance between two use of the module beyond which module is released
      self.__max_reuse_dist_in_numel: int = max_reuse_distance_in_numel
      # queue for parameters to fetch. parameters will be popped off the left
      # side of the dequeue as they are fetched
      self.__param_queue: Deque[__class__.__ParamInTrace] = None
      self.__prefetch_bucket_sz: int = prefetch_bucket_sz
      self.__prefetch_nvme: bool = prefetch_nvme
      self.hierarchy: int = 0
  
      # stream that will be used for allgather operations
      self.__allgather_stream: Stream = allgather_stream
  
      # limit the number of fetch events that can be queued at once
      # otherwise, what happens is memory is allocated by the host thread at the
      # time of the call, but not used until later by the asynchronous cuda stream.
      # allowing an infinite number of these to queue up causes a lot of memory
      # pressure that then becomes detrimental to performance.
      # this is a much less elegant way of fixing this vs something like using
      # cudaMallocAsync/cudaFreeAsync. Choosing to not expose this to the user now
      # because ideally in the future its replaced by an async allocation
      # mechanism which doesn't require any configuration by the user.
      self.__ongoing_fetch_events: Deque[Event] = collections.deque()
      # TODO. make this configurable via JSON
      self.__max_ongoing_fetch_events: int = 2
  
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 1 to create a new PartitionedParameterCoordinator object  with example: obj = PartitionedParameterCoordinator(prefetch_bucket_sz, max_reuse_distance_in_numel, max_available_parameters_in_numel, allgather_stream, prefetch_nvme)