Support CheckpointInputPipelineHook in Estimator MirrorStrategy
@ZhangYaoFu ,

We are sorry to inform you that for 1.x versions there will not be any Feature additions. Its support already dropped and new features won't be added.Even estimator API also going to be deprecated soon. 

You can migrate to 2.x versions and if found any feature needed with 2.x versions then the same will be reviewed. You can find migration guide [here](https://www.tensorflow.org/guide/migrate/migrate_tf2).

Thanks! @SuryanarayanaY  
Thank you for reply
How to save pipeline info in checkpoint in 2.x version?
 @ZhangYaoFu , 

Could you please check this [API](https://www.tensorflow.org/ranking/api_docs/python/tfr/keras/pipeline/ModelFitPipeline) is helpful for your requirement. This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you. This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further.
<details><summary>Click to expand!</summary> 
 
 ### Issue Type

Feature Request

### Have you reproduced the bug with TF nightly?

Yes

### Source

binary

### Tensorflow Version

tf 1.15

### Custom Code

No

### OS Platform and Distribution

_No response_

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

_No response_

### GCC/Compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current Behaviour?

Failed to save pipeline info in checkpoint.

### Standalone code to reproduce the issue

```shell
mirrored_strategy = tf.distribute.MirroredStrategy()
    run_config = tf.estimator.RunConfig(
        model_dir=output_dir,
        save_checkpoints_steps=save_checkpoints_steps,
        train_distribute=mirrored_strategy,
        eval_distribute=mirrored_strategy,
        keep_checkpoint_max=max_ckp_num
    )

estimator.train(input_fn=input_fn, 
                hooks=[tf.data.experimental.CheckpointInputPipelineHook(estimator)],
                       steps=tf_args.test_train_step)
```


### Relevant log output

```shell
2023-05-03 21:53:52.048712: W tensorflow/core/framework/op_kernel.cc:1651] OP_REQUIRES failed at iterator_ops.cc:1081 : Failed precondition: RemoteCall is stateful.
```
</details>