code: def initialize_communicator(
      group_key,
      rank,
      group_size,
      communication_hint="auto",
      timeout_seconds=0,
  ):
      """Initializes a collective communicator.
  
      This creates a collective communicator, which represents membership to a
      collective group identified by the group_key. It should be called once per
      member of the group, and each member needs to be on a different device.
      It blocks until all members of the group run this op.
  
      Communicators of a group can only be initialized once. Trying to initialize
      communicators for an existing group key will result in an error.
  
      Args:
        group_key: an int32 `tf.Tensor` identifying the group.
        rank: an `tf.Tensor` specifying the rank of this device in the group. If
          specified, the rank is required to be unique in the group.
        group_size: an int32 `tf.Tensor`. The size of the group.
        communication_hint: preferred collective communication.  The implementation
          may fall back to another mechanism.  Options include `auto`, `ring`, and
          `nccl`.
        timeout_seconds: If set to a non zero, set a completion timeout to detect
          staleness. If the timer goes off, a DeadlineExceededError is raised. The
          timeout value in seconds. This feature is experimental.
  
  
      Returns:
        A resource `tf.Tensor`.
      """
      return gen_collective_ops.collective_initialize_communicator(
          group_key=group_key,
          rank=rank,
          group_size=group_size,
          communication_hint=communication_hint,
          timeout_seconds=timeout_seconds,
      )
  
file_path: tensorflow-upstream/tensorflow/python/ops/collective_ops.py
imports: from tensorflow.python.ops import gen_collective_ops
label_desc: Use image node_id 10 for calling a global function with example usage: initialize_communicator(group_key, rank, group_size, communication_hint, timeout_seconds) and returns: gen_collective_ops