code: def initialize_accelerator_system(
      device_type: Optional[str] = None,
      enable_coordination_service: Optional[bool] = True,
      num_logical_cpu_devices: Optional[int] = None,
      experimental_reset_context: Optional[bool] = False,
      experimental_enable_megcore: Optional[bool] = False,
  ) -> str:
      """Initializes accelerators and communication fabrics for DTensor.
  
      DTensor configures TensorFlow to run in the local mode or multi-client mode.
      - In local mode, a mesh can only use devices attached to the current process.
      - In multi-client mode, a mesh can span across devices from multiple clients.
  
      If `DTENSOR_JOBS` is non-empty, DTensor configures TensorFlow to run in the
      multi-client mode using the distributed runtime. In multi-client mode devices
      on different clients can communicate with each other.
  
      The following environment variables controls the behavior of this function.
  
      - `DTENSOR_JOBS`: string, a comma separated list. Each item in the list is
          of format `{hostname}:{port}`. If empty, DTensor runs in the local mode.
          Examples of valid `DTENSOR_JOBS` values:
          - 4 clients on localhost:
            `localhost:10000,localhost:10001,localhost:10002,localhost:10003`
          - 2 clients on host1, 2 clients on host2
            `host1:10000,host1:10001,host2:10000,host2:10003`
          If the hostnames are BNS addresses, the items must be sorted in
          alphabetical order.
      - `DTENSOR_CLIENT_ID`: integer, between `0` to `num_clients - 1`, to identify
          the client id of the current process. The default value is `0`.
      - `DTENSOR_JOB_NAME`: string, a string for the name of the TensorFlow job.
          The job name controls the job name section of the TensorFlow DeviceSpecs,
          e.g., `job:worker` in `/job:worker/replica:0/task:0/device:TPU:0` when
          the job name is `worker`.
          The default value is `localhost` in local mode, and
          `worker` when in the multi-client mode. All DTensor clients within the
          same multi-client cluster share the same job name.
      - `DTENSOR_USE_PARALLEL_EXECUTOR`: string, with its value being `pw` to
          specify that the backend is Pathways, and TensorFlow otherwise.
  
      Args:
        device_type: Type of accelerator to use, can be CPU, GPU, or TPU. If None,
          uses `tf.experimental.dtensor.preferred_device_type()`.
        enable_coordination_service: If true, enable distributed coordination
          service to make sure that workers know the devices on each other, when
          there is more than 1 client.
        num_logical_cpu_devices: the number of logical CPU devices per DTensor
          client. Default to the current number of logical CPU
          (`dtensor.num_local_devices("CPU")`),when `device_type` is CPU, otherwise
          set automatially to match the number of local GPU/TPU devices.
        experimental_reset_context: Reset the tensorflow context. Behaviors of
          existing TensorFlow objects (e.g. Tensors) are undefined. Set this to True
          as an escape hatch, if there is no clear way to refactor your code to call
          initialize_accelerator_system() before calling TensorFlow APIs that
          initialize the context.
        experimental_enable_megcore: Optionally enable megcore in backend.
  
      Returns:
        device_type: the type of accelerator that was initialized.
      """
      global _INITIALIZED_ACCELERATOR_SYSTEM_TYPE
      assert context.executing_eagerly()
  
      if is_initialized():
          raise ValueError(
              "Accelerator system has already been initialized. "
              "Call tf.experimental.dtensor.shutdown_accelerator_system() first."
          )
  
      if experimental_reset_context:
          if (
              context.context()._initialized
          ):  # pylint: disable=protected-access
              logging.warn(
                  "experimental_reset_context is True. "
                  "Resetting TensorFlow context. Existing TensorFlow objects "
                  "(e.g. Tensors and resources) are invalidated."
              )
              context.context().ensure_uninitialized()
  
      if (
          context.context()._initialized
      ):  # pylint: disable=protected-access
          raise ValueError(
              "TensorFlow has already been initialized. "
              "tf.experimental.dtensor.initialize_accelerator_system() must be "
              "called before TensorFlow is initialized."
          )
  
      context.context()._clear_caches()  # pylint: disable=protected-access
  
      if device_type is None:
          device_type = config.preferred_device_type()
  
      device_type = device_type.upper()
      if device_type not in {"CPU", "GPU", "TPU"}:
          raise ValueError(
              f"Unknown device_type {device_type}. "
              "Allowed values are CPU, GPU, or TPU"
          )
  
      if config.gpu_use_nccl_communication():
          logical_gpu_count = config.num_local_devices("GPU")
          physical_gpu_count = len(
              tf_config.list_physical_devices("GPU")
          )
          if logical_gpu_count > physical_gpu_count:
              raise ValueError(
                  "DTENSOR_GPU_USE_NCCL_COMMUNICATION is set for using NCCL. "
                  "NCCL Collectives require one to one mapping between logical and "
                  "physical GPUs. "
                  f"The number of logical GPU ({logical_gpu_count}) "
                  f"is more than the number of physical GPU ({physical_gpu_count})."
              )
  
      # Configure logical host CPU devices for accelerators.
      if device_type in ("GPU", "TPU"):
          num_local_devices = config.num_local_devices(device_type)
          if num_logical_cpu_devices is None:
              num_logical_cpu_devices = max(
                  config.num_local_devices("CPU"), num_local_devices
              )
          else:
              if num_logical_cpu_devices < num_local_devices:
                  raise ValueError(
                      "If set, `num_logical_cpu_devices`"
                      f" (={num_logical_cpu_devices}) must be greater than or"
                      f" equal to the number of local {device_type} devices"
                      f" (={num_local_devices})"
                  )
  
      if num_logical_cpu_devices is not None:
          tf_config.set_logical_device_configuration(
              tf_config.list_physical_devices("CPU")[0],
              [context.LogicalDeviceConfiguration()]
              * num_logical_cpu_devices,
          )
  
      if not config.is_local_mode():
          initialize_multi_client_cluster(
              job_name=config.job_name(),
              dtensor_jobs=config.jobs(),
              client_id=config.client_id(),
              collective_leader=config.full_job_name(task_id=0),
              gpu_use_nccl_communication=config.gpu_use_nccl_communication(),
              enable_coordination_service=enable_coordination_service,
          )
      else:
          if device_type == "GPU":
              # Enables Nccl on local mode.
              context.context()._collective_use_nccl_communication = (  # pylint: disable=protected-access
                  config.gpu_use_nccl_communication()
              )
  
      if device_type == "TPU" and not config.backend_is_pw():
          tpu_util.initialize_tpu_system(
              use_megacore=experimental_enable_megcore
          )
  
      _INITIALIZED_ACCELERATOR_SYSTEM_TYPE = device_type
  
      return device_type
  
file_path: tensorflow-upstream/tensorflow/dtensor/python/accelerator_util.py
imports: from typing import List, Optional
  from absl import logging
  from tensorflow.core.protobuf import cluster_pb2
  from tensorflow.core.protobuf import tensorflow_server_pb2
  from tensorflow.dtensor.python import config
  from tensorflow.dtensor.python import tpu_util
  from tensorflow.python.eager import context
  from tensorflow.python.framework import config
  from tensorflow.python.platform import remote_utils
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 4 for calling a global function with example usage: initialize_accelerator_system(device_type, enable_coordination_service, num_logical_cpu_devices, experimental_reset_context, experimental_enable_megcore) and returns: device_type