code: def my_all_reduce(
      t,
      group_size,
      group_key,
      instance_key,
      merge_op,
      final_op,
      subdiv_offsets=(0,),
      communication_hint="auto",
  ):
      """Reduces tensors collectively, across devices.
  
      Args:
        t: the tensor to be reduced.
        group_size: the total number of tensors to be collectively reduced.
          Each must reside on a different device.  Should be a positive integer.
        group_key: an integer identifying the group of devices.
        instance_key: an integer identifying the participating group of Ops.
        merge_op: string naming the binary Op to be applied to compute each
          partial reduction.
        final_op: string naming the unary Op to be applied to each fully
          reduced value.  Can be 'Id' for no operation.
        subdiv_offsets: a list of integer offsets into the tensor at which each
          independent subdivision should begin.  Use [0] if no subdivision should
          be done.
        communication_hint: preferred collective communication.  The implementation
          may fall back to another mechanism.  Options include `auto`, `ring`, and
          `nccl`.
  
      Returns:
        An Op implementing the distributed reduction.
  
      Raises:
        ValueError: if any of the input parameter constraints are not met.
      """
      if group_size < 1:
          raise ValueError(
              "Parameter group_size to all_reduce must be at least 1."
          )
      # return gen_collective_ops.collective_reduce(
      #     t,
      #     group_size=group_size,
      #     group_key=group_key,
      #     instance_key=instance_key,
      #     merge_op=merge_op,
      #     final_op=final_op,
      #     subdiv_offsets=subdiv_offsets,
      #     communication_hint=communication_hint.lower())
      return my_collective_reduce(
          t,
          group_size=group_size,
          group_key=group_key,
          instance_key=instance_key,
          merge_op=merge_op,
          final_op=final_op,
          subdiv_offsets=subdiv_offsets,
          communication_hint=communication_hint.lower(),
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import enum
  import six
  from tensorflow.python.client import device_lib
  from tensorflow.python.distribute import cross_device_utils
  from tensorflow.python.distribute import device_util
  from tensorflow.python.distribute import reduce_util
  from tensorflow.python.distribute import values
  from tensorflow.python.eager import context
  from tensorflow.python.framework import kernels
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import resource_variable_ops
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.util.tf_export import tf_export
  from tensorflow.tools.docs import doc_controls
  import tensorflow
  from tensorflow.python.distribute.cross_device_ops import CollectiveAllReduce
  import tensorflow.python.distribute.cross_device_ops
  from byteps.tensorflow.ops import broadcast, _push_pull
  from byteps.tensorflow import local_rank, size
label_desc: Use image node_id 8 for calling a global function with example usage: my_all_reduce(t, group_size, group_key, instance_key, merge_op, final_op, subdiv_offsets, communication_hint) and returns: my_collective_reduce