docstr: Sets up TensorFLow distributed environment and initializes the model.
docstr_tok: ["Sets","up","TensorFLow","distributed","environment","and","initializes","the","model","."]
code: def setup_distributed(self, cluster):
      """
      Sets up TensorFLow distributed environment and initializes the model.
      """
      worker_cluster, node_cluster = cluster
      self.rank = get_rank(node_cluster)
      logger.info("cluster is: {}".format(node_cluster))
  
      os.environ["TF_CONFIG"] = json.dumps(
          {
              "cluster": {"worker": worker_cluster},
              "task": {"type": "worker", "index": self.rank},
          }
      )
      ips = set([node.split(":")[0] for node in worker_cluster])
      os.environ["no_proxy"] = ",".join(ips)
  
      self.strategy = (
          tf.distribute.experimental.MultiWorkerMirroredStrategy()
      )
  
imports: import json
  import os
  import tempfile
  import shutil
  import copy
  import tensorflow
  from bigdl.orca.data.utils import partition_get_data_label
  from bigdl.orca.data.file import exists
  from bigdl.orca.learn.utils import save_pkl, duplicate_stdout_stderr_to_file, get_rank, process_tensorboard_in_callbacks, save_model, load_model, replace_specific_object_from_callbacks
  from bigdl.orca.learn.log_monitor import LogMonitor
  from bigdl.orca.learn.tf2.callbacks import ModelCheckpoint
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 4 for calling the SparkRunner obj's underlying member method code with example usage: obj.setup_distributed(cluster) without return types