class_docstr: Management of worker memory usage
  
  Parameters
  ----------
  worker
      Worker to manage
  
  For meaning of the remaining parameters, see the matching
  parameter names in :class:`~.distributed.worker.Worker`.
  
  Notes
  -----
  
  If data is a callable and has the argument ``worker_local_directory`` in its
  signature, it will be filled with the worker's attr:``local_directory``.
code: def _to_dict(self, *, exclude: Container[str] = ()) -> dict:
      info = {
          k: v
          for k, v in self.__dict__.items()
          if not k.startswith("_")
      }
      info["data"] = dict.fromkeys(self.data)
      return info
  
imports: from __future__ import annotations
  import asyncio
  import logging
  import os
  import sys
  import warnings
  from collections.abc import Callable, Container, Hashable, MutableMapping
  from contextlib import suppress
  from functools import partial
  from typing import TYPE_CHECKING, Any, Literal, Union, cast
  import psutil
  import dask.config
  from dask.system import CPU_COUNT
  from dask.typing import Key
  from dask.utils import format_bytes, parse_bytes, parse_timedelta
  from distributed import system
  from distributed.compatibility import WINDOWS, PeriodicCallback
  from distributed.core import Status
  from distributed.metrics import context_meter, monotonic
  from distributed.spill import ManualEvictProto, SpillBuffer
  from distributed.utils import RateLimiterFilter, has_arg, log_errors
  from distributed.utils_perf import ThrottledGC
label_desc: Use image node_id 3 for calling the WorkerMemoryManager obj's underlying member method code with example usage: obj._to_dict() and returns: info