code: def get_replicator(accelerator: Optional[str]) -> Replicator:
      """Returns a replicator instance appropriate for the given accelerator.
  
      This caches the instance using functools.cache, so that only one replicator
      is instantiated per process and argument value.
  
      Args:
        accelerator: None, 'TPU', 'GPU', or 'CPU'. If None, the first available
          accelerator type will be chosen from ('TPU', 'GPU', 'CPU').
  
      Returns:
        A replicator, for replciating weights, datasets, and updates across
        one or more accelerators.
      """
      if accelerator:
          accelerator = _ensure_accelerator(accelerator)
      else:
          accelerator = _get_first_available_accelerator_type()
  
      if accelerator == "TPU":
          tf.tpu.experimental.initialize_tpu_system()
          return snt.distribute.TpuReplicator()
      else:
          return snt.distribute.Replicator()
  
imports: import copy
  import dataclasses
  import functools
  from typing import Iterator, List, Optional, Tuple, Union, Sequence
  from acme import adders
  from acme import core
  from acme import datasets
  from acme import specs
  from acme import types
  from acme.adders import reverb
  from acme.agents import agent
  from acme.agents.tf import actors
  from acme.agents.tf.d4pg import learning
  from acme.tf import networks
  from acme.tf import utils
  from acme.tf import variable_utils
  from acme.utils import counting
  from acme.utils import loggers
  import reverb
  import sonnet
  import tensorflow
label_desc: Use image node_id 3 for calling a global function with example usage: get_replicator(accelerator) and returns: snt, snt