tf.train.Checkpoint.restore does not restore the tf.data.Iterator state.
###  Code for the issue (CoLab) :
```
db_iter = iter(dataset)
# ...
for epoch in range(10):
    manager.restore_or_initialize()
    # ...
    db_iter = iter(dataset)  # Reset the iterator
    # ...
```
The iterator is reset similarly at the beginning of each epoch by reassigning db_iter = iter(dataset). However, there's a subtle issue in this code. After the iterator is reset in the inner loop, it is no longer used in the next iteration because it's overwritten with a new iterator. This means that the checkpoint saves the initial iterator state, but it doesn't affect the dataset iteration because the iterator is recreated in each iteration

To solve this problem I have used it as 
```
iterator = iter(dataset)
# ...
for epoch in range(10):
    manager.restore_or_initialize()
    iterator = iter(dataset)  # Reset the iterator
    # ...
```
### CODE
``` import os
import numpy as np
import tensorflow as tf
class Net(tf.keras.Model):
    def __init__(self):
        super(Net, self).__init__()
        self.x = tf.Variable(tf.keras.initializers.GlorotUniform()(shape=[1]))

    def call(self, inputs):
        return inputs


def main():
    tf.random.set_seed(123)
    net = Net()
    optim = tf.optimizers.Adam(learning_rate=0.001)
    dataset = tf.data.Dataset.from_tensor_slices((np.arange(10)))
    dataset = dataset.shuffle(10, reshuffle_each_iteration=True)
    dataset = dataset.batch(2)
    # Create a TensorFlow iterator for the dataset
    iterator = iter(dataset)
    step = tf.Variable(0)
    checkpoint = tf.train.Checkpoint(
        step=step, optimizer=optim, net=net
    )
    manager = tf.train.CheckpointManager(checkpoint, "./ckpts", max_to_keep=50)
    manager.restore_or_initialize()
    print(step)
    for epoch in range(10):
        manager.restore_or_initialize()
        iterator = iter(dataset)  # Reset the iterator
        for _ in range(dataset.cardinality()):
            batch = next(iterator)
            step.assign_add(1)
            print(batch)
        manager.save()
        print(f"Epoch {epoch} finished.")

if __name__ == "__main__":
    main()
```

 Hi @arjun988 . Thank you for your comment. However, I failed to get your point. I am elaborating below : 

**My code snippet**
```

for epoch in range(10):
        manager.restore_or_initialize()
        for _ in range(dataset.cardinality()):
            batch = next(db_iter)
            step.assign_add(1)
            print(batch)
        db_iter = iter(dataset)
        manager.save()
        print(f"Epoch {epoch} finished.")
```
Let's see it from Epoch 1

**Epoch 1**

1. `manager`  performs initialization as there are no checkpoints in the beginning.
2. `db_iter` is in an initialized atate.
3. At the end `db_iter` is reset. 
4. `manager.save()` causes the current state of `db_iter` which is initialied, to be saved.

**Epoch 2**

1. `manager` now restores the first checkpoint and thus `db_iter` should be in an initialized state.

However, I would get a `StopIterationError`. Why is that ? `db_iter` is in an initialized state.

By the way, the whole point of saving the iterator is to be able to reproduce experiments when fine-tuning from a checkpoint. Hi @ujjwalnur 
In your code snippet, you correctly reset db_iter to the beginning of the dataset after each epoch using db_iter = iter(dataset). However, when you save the checkpoint using manager.save(), it's saving the state of the iterator at that point, which is at the beginning of the dataset.

When you restore the checkpoint at the start of the next epoch using manager.restore_or_initialize(), it correctly restores the state of the iterator to where it was at the end of the previous epoch (i.e., the beginning of the dataset). However, when you subsequently call next(db_iter) inside the epoch loop, it will attempt to get the next element from the iterator, which is at the beginning of the dataset. This is why you are getting a StopIterationError because you're trying to iterate beyond the end of the dataset.

Here is the code for the same 
```

db_iter = iter(dataset)  # Move this line here, before the epoch loop

for epoch in range(10):
    manager.restore_or_initialize()
    for _ in range(dataset.cardinality()):
        batch = next(db_iter)
        step.assign_add(1)
        print(batch)
    manager.save()
    print(f"Epoch {epoch} finished.")

```

With this change, the iterator is reset to the beginning of the dataset only once at the beginning of the training process, and it will correctly continue the iteration after restoring the checkpoint. > db_iter = iter(dataset)  # Move this line here, before the epoch loop
> 
> for epoch in range(10):
>     manager.restore_or_initialize()
>     for _ in range(dataset.cardinality()):
>         batch = next(db_iter)
>         step.assign_add(1)
>         print(batch)
>     manager.save()
>     print(f"Epoch {epoch} finished.")
> ```
> 
> With this change, the iterator is reset to the beginning of the dataset only once at the beginning of the training process, and it will correctly continue the iteration after restoring the checkpoint.

If you move line `db_iter = iter(dataset)`  to the top, it will never iterate beyond the first epoch since `db_iter` will be exhausted. Setting `db_iter = iter(dataset)` is a compulsion after the end of an epoch since we are working with an iterator directly. 

Therefore, I cannot move `db_iter = iter(dataset)` outside the inner loop in my original code. 

About `manager.restore_or_initialize()` won't be called at the start of every epoch. It will be called once during the beginning of the script. The `manager.restore_or_initialize()` inside the loop was just a result of some testing.  In reality, 
it will be more like the following : 

```
manager.restore_or_initialize()
for epoch in range(10):
    ....
```
In the description ( thanks a lot !! ) you gave, I was able to follow you nicely until the very last line. If you note, you say that 

> This is why you are getting a StopIterationError because you're trying to iterate beyond the end of the dataset.

While, the reality is that the iterator is at the beginning of the dataset. It is either I am not catching some very basic concept or there is something not quite right about  restoration of the iterator. Thanks a lot your explanation have helped , I am sorry for any Confusion caused, Let's talk over this 
You are correct in your original code, you have the line db_iter = iter(dataset) inside the epoch loop, which resets the iterator to the beginning of the dataset at the start of each epoch. This is a valid approach when working with iterators in this manner.

The StopIterationError occurs because when you restore the checkpoint, it correctly restores the iterator's state to where it was at the end of the previous epoch, which is at the beginning of the dataset. The error happens because, during the epoch loop, you attempt to get the next element from the iterator using batch = next(db_iter), but there are no elements left to iterate in the dataset since it's already at the beginning. Try to expand after this what are the different approach you have thought .
 Hi @ujjwalnur ,

I have replicated the issue and attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/e5e6d694828f1213ef8b916823199636/62006.ipynb) here. Please check from your side to check is it the same issue you are trying to bring.

During second call all the code executing again starting from `dataset` creation to `iter(dataset)` etc. But it also surprised me why iter(dataset) not working even though we ran the complete code again. I am not sure at this point whether this is intended but as per my preliminary observations there seems some issue. Will dig into more or escalate to the concern SME.

Thanks!
### Issue type

Bug

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

No

### Source

binary

### TensorFlow version

tf 2.13.0

### Custom code

Yes

### OS platform and distribution

Ubuntu 22.04

### Mobile device

_No response_

### Python version

3.10

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

11.8

### GPU model and memory

_No response_

### Current behavior?

`tf.data.Dataset` iterators are supported in `tf.train.Checkpoint`.  When I try to restore a checkpoint, I always get a `StopIteration` error.  This should not be the case because : 

1. I save the checkpoint only at the end of an epoch.
2. Before saving the checkpoint, I always call `db_iter=iter(dataset)` ,so it should give me a new iterator.

In prior versions of TF there were functions like `make_initializable_iterator()` which are now deprecated. Since, there is no native mechanism to reset a python iterator to beginning, I wonder why `tf.train.Checkpoint.restore()` does not save the iterator state properly.

I also understand well that one can just loop over the dataset like:

```
import tensorflow as tf
ds = tf.data.Dataset.range(50)
for index, data in ds:
    # Do something 
```

However, saving the dataset in the checkpoint has two problems:

a) **I have not explicitly tested this**  For a very large dataset like MSCOCO or OpenImages or even Imagenet, what does saving the dataset mean , if I already have the dataset as TFRecords ? Will it end up saving the whole dataset again inside the  checkpoint ? At least, `tf.data.Dataset.save()` points towards this. In case this is it, I would never like to save a large dataset directly into the checkpoint.

b) **This is fully tested** If I experiment with a small simple dataset ( e.g:- `tf.data.Dataset.range(10)` ) and save it in the checkpoint, then it does not restore the state of the iterator. This observation is the same as #48178 which is still unresolved. 

### Standalone code to reproduce the issue

```shell
import os
import numpy as np
import tensorflow as tf
from absl import app


class Net(tf.keras.Model):
    def __init__(self):
        super(Net, self).__init__()
        self.x = tf.Variable(tf.keras.initializers.GlorotUniform()(shape=[1]))

    def call(self, inputs):
        return inputs


def main(argv):
    del argv
    tf.random.set_seed(123)
    net = Net()
    optim = tf.optimizers.Adam(learning_rate=0.001)

    dataset = tf.data.Dataset.from_tensor_slices((np.arange(10)))
    dataset = dataset.shuffle(10, reshuffle_each_iteration=True)
    dataset = dataset.batch(2)
    db_iter = iter(dataset)
    step = tf.Variable(0)
    checkpoint = tf.train.Checkpoint(
        step=step, optimizer=optim, net=net, db_iter=db_iter
    )
    manager = tf.train.CheckpointManager(checkpoint, "./ckpts", max_to_keep=50)
    manager.restore_or_initialize()
    print(step)
    for epoch in range(10):
        manager.restore_or_initialize()
        for _ in range(dataset.cardinality()):
            batch = next(db_iter)
            step.assign_add(1)
            print(batch)
        db_iter = iter(dataset)
        manager.save()
        print(f"Epoch {epoch} finished.")


if __name__ == "__main__":
    app.run(main)
```
```


### Relevant log output

```shell
The first time, it will print something like : 

<tf.Variable 'Variable:0' shape=() dtype=int32, numpy=0>
tf.Tensor([3 6], shape=(2,), dtype=int64)
tf.Tensor([4 0], shape=(2,), dtype=int64)
tf.Tensor([1 5], shape=(2,), dtype=int64)
tf.Tensor([7 8], shape=(2,), dtype=int64)
tf.Tensor([2 9], shape=(2,), dtype=int64)
Epoch 0 finished.
tf.Tensor([6 3], shape=(2,), dtype=int64)
tf.Tensor([0 5], shape=(2,), dtype=int64)
tf.Tensor([2 8], shape=(2,), dtype=int64)
tf.Tensor([1 7], shape=(2,), dtype=int64)
tf.Tensor([4 9], shape=(2,), dtype=int64)
Epoch 1 finished.
tf.Tensor([0 3], shape=(2,), dtype=int64)
tf.Tensor([1 4], shape=(2,), dtype=int64)
tf.Tensor([6 7], shape=(2,), dtype=int64)
tf.Tensor([9 8], shape=(2,), dtype=int64)
tf.Tensor([5 2], shape=(2,), dtype=int64)
Epoch 2 finished.
tf.Tensor([3 7], shape=(2,), dtype=int64)
tf.Tensor([0 9], shape=(2,), dtype=int64)
tf.Tensor([4 2], shape=(2,), dtype=int64)
tf.Tensor([1 8], shape=(2,), dtype=int64)
tf.Tensor([5 6], shape=(2,), dtype=int64)

and so on....


From next time onwards:

    raise StopIteration
StopIteration
```
