class_docstr: Scheduler extension that optimizes memory usage across the cluster.
  It can be either triggered by hand or automatically every few seconds; at every
  iteration it performs one or both of the following:
  
  - create new replicas of in-memory tasks
  - destroy replicas of in-memory tasks; this never destroys the last available copy.
  
  There are no 'move' operations. A move is performed in two passes: first you create
  a copy and, in the next iteration, you delete the original (if the copy succeeded).
  
  This extension is configured by the dask config section
  ``distributed.scheduler.active-memory-manager``.
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docstr: Return True if the AMM is being triggered periodically; False otherwise
docstr_tok: ["Return","True","if","the","AMM","is","being","triggered","periodically",";","False","otherwise"]
code: def running(self) -> bool:
      """Return True if the AMM is being triggered periodically; False otherwise"""
      return f"amm-{id(self)}" in self.scheduler.periodic_callbacks
  
imports: from __future__ import annotations
  import abc
  import logging
  from collections import defaultdict
  from collections.abc import Generator
  from typing import TYPE_CHECKING, Any, Literal, NamedTuple, Union
  import dask
  from dask.typing import Key
  from dask.utils import parse_timedelta
  from distributed import client
  from distributed import scheduler
  from distributed.compatibility import PeriodicCallback
  from distributed.core import Status
  from distributed.metrics import time
  from distributed.utils import import_term, log_errors
label_desc: Use image node_id 5 for calling the ActiveMemoryManagerExtension obj's underlying member method code with example usage: obj.running() and returns: unknown