incorrect result run  collective_ops.all_reduce with communication_hint = nccl
run this demo in two process plz @Jiaao-Bai Could you please verify that the NCCL library is installed correctly and that its version is compatible with the TensorFlow version. Check for any environment variables or configuration settings that might affect NCCL communication.
Please try with the latest tensorflow version as older versions are not recommended as well. Thank you! i add control_dependencies and result is correct， thanks

```
"""Illustrate AllReduce"""

import multiprocessing as mp
import logging
import time
import threading
import tensorflow as tf
from tensorflow.python.ops import collective_ops
import os
import json
import datetime
from tensorflow.python.framework import ops
from typing import Callable, List, Optional, Union
from tensorflow.python.types import core
from tensorflow.python.client import timeline

FORMAT = '%(asctime)s, %(levelname)-4s [%(filename)s:%(lineno)d] %(message)s'
logging.basicConfig(format=FORMAT, level=logging.INFO)
tf.compat.v1.disable_eager_execution()

os.environ['TF_CONFIG'] = json.dumps({
  'cluster': {
      'worker': ["jscs-ai-nn-28:12345","jscs-ai-nn-28:23456"]
  },
  'task': {'type': 'worker', 'index': 0}
})

MP_METHOD = 'fork'
NUM_PROCESSES = 2
CHIEF_INDEX = 0
JOB = 'worker'
DELAY = 0.01

def control_input(control_input: Union[core.TensorLike,
                                              ops.Operation]):
  if control_input is not None:
    return ops.control_dependencies([control_input])
  return ops.NullContextmanager()


def process_fn():
  """allreduce process"""
  cluster_resolver = tf.distribute.cluster_resolver.TFConfigClusterResolver()
  cluster_spec = cluster_resolver.cluster_spec()
  task_index = cluster_resolver.task_id


  config = tf.compat.v1.ConfigProto()
  config.experimental.collective_group_leader = "/job:worker/replica:0/task:0"
  server = tf.compat.v1.train.Server(cluster_spec,
                                     config=config,
                                     job_name='worker',
                                     task_index=task_index)
  with tf.Graph().as_default():
    sync_ops = []
    for i in range(100):
      token = tf.compat.v1.get_variable(name = str(i), shape = [128, 4])
      if sync_ops:
         depend = sync_ops[-1]
      else:
         depend = None
      with control_input(depend):
        sync_op = collective_ops.all_reduce(
            token, NUM_PROCESSES, 0, i, 'Add', 'Div', (0,), 'nccl')
        sync_ops.append(sync_op)

    with tf.compat.v1.Session(
            target=server.target,
            config = config) \
            as mon_sess:
      time.sleep((task_index + 1) * DELAY)
      run_metadata = tf.compat.v1.RunMetadata()
      profile_options = tf.compat.v1.RunOptions(trace_level=tf.compat.v1.RunOptions.FULL_TRACE)

      gvi = tf.compat.v1.global_variables_initializer()
      mon_sess.run(gvi)
      batch = 0
      start_time = datetime.datetime.now()
      print("begin to run")
      while 1:
        ret = mon_sess.run(sync_ops, options=profile_options, run_metadata=run_metadata)  # successful
        batch += 1
        if batch % 1 == 0:
            tl = timeline.Timeline(run_metadata.step_stats)
            ctf = tl.generate_chrome_trace_format()
            timline_json = "./timeline_{}_{}.json".format(task_index, batch)
            with open(timline_json, 'w+') as f:
                f.write(ctf)
            delta = datetime.datetime.now() - start_time
            start_time = datetime.datetime.now()
            print('batch {}, delta {}.'.format(batch, delta.total_seconds()))
            return
        else:
            print('batch {}.'.format(batch))

def start_process():
    """start process"""
    process_fn()

if __name__ == '__main__':
  start_process()
``` Are you satisfied with the resolution of your issue?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62493">Yes</a>
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=No&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/62493">No</a>
 @Jiaao-Bai Glad the issue has been resolved!
Thank you for the confirmation.
### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

No

### Source

binary

### TensorFlow version

tf2.8

### Custom code

Yes

### OS platform and distribution

ubuntu 20.04

### Mobile device

not mobile device

### Python version

3.10.6

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

the result is wrong
should be [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]
### Standalone code to reproduce the issue

```shell
"""Illustrate AllReduce"""

import multiprocessing as mp
import logging
import time
import threading
import tensorflow as tf
from tensorflow.python.ops import collective_ops
import os
import json
import datetime


FORMAT = '%(asctime)s, %(levelname)-4s [%(filename)s:%(lineno)d] %(message)s'
logging.basicConfig(format=FORMAT, level=logging.INFO)
tf.compat.v1.disable_eager_execution()

os.environ['TF_CONFIG'] = json.dumps({
  'cluster': {
      #change this
      'worker': ["jscs-ai-nn-28:12345","jscs-ai-nn-28:23456"]
  },
  #change this
  'task': {'type': 'worker', 'index': 0}
})

MP_METHOD = 'fork'
NUM_PROCESSES = 2
CHIEF_INDEX = 0
JOB = 'worker'
DELAY = 0.01

def process_fn():
  """allreduce process"""
  cluster_resolver = tf.distribute.cluster_resolver.TFConfigClusterResolver()
  cluster_spec = cluster_resolver.cluster_spec()
  task_index = cluster_resolver.task_id


  config = tf.compat.v1.ConfigProto()
  config.experimental.collective_group_leader = "/job:worker/replica:0/task:0"
  server = tf.compat.v1.train.Server(cluster_spec,
                                     config=config,
                                     job_name='worker',
                                     task_index=task_index)
  with tf.Graph().as_default():
    sync_ops = []
    for i in range(10):
      # token = tf.compat.v1.get_variable(name = str(i), shape = [2, 3])
      token = tf.constant(i, dtype=tf.float32, name = str(i))
      sync_op = collective_ops.all_reduce_v2(
          token, NUM_PROCESSES, 0, i, 'Add', 'Div', 'nccl')
      sync_ops.append(sync_op)

    with tf.compat.v1.Session(
            target=server.target,
            config = config) \
            as mon_sess:
      time.sleep((task_index + 1) * DELAY)
      gvi = tf.compat.v1.global_variables_initializer()
      mon_sess.run(gvi)
      batch = 0
      start_time = datetime.datetime.now()
      print("begin to run")
      while 1:
        ret = mon_sess.run(sync_ops)  # successful
        print(ret)
        batch += 1
        if batch % 1 == 0:
            delta = datetime.datetime.now() - start_time
            start_time = datetime.datetime.now()
            print('batch {}, delta {}.'.format(batch, delta.total_seconds()))
            return
        else:
            print('batch {}.'.format(batch))

def start_process():
    """start process"""
    process_fn()

if __name__ == '__main__':
  start_process()
```


### Relevant log output

```shell
begin to run
[0.5, 4.0, 2.5, 6.0, 3.0, 2.5, 5.0, 6.0, 7.0, 8.5]
batch 1, delta 1.517457.
```
