tf.data Dataset: Warning when caching validation set. "You should use `dataset.take(k).cache().repeat()` instead."
@munsteraner 
In order to expedite the trouble-shooting process, please provide the complete code snippet to reproduce the issue reported here. I tried to replicate the issue reported [here](https://colab.research.google.com/gist/sushreebarsa/f58fda9edac09f76d7016a66cf691452/61160.ipynb). Thank you! 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. Are you satisfied with the resolution of your issue?
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### Issue type

Bug

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

No

### Source

source

### TensorFlow version

tf 2.9.0

### Custom code

Yes

### OS platform and distribution

Ubuntu 22.04 LTS

### Mobile device

_No response_

### Python version

3.9.5

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

cuda_11.3.r11.3/compiler.29920130_0

### GPU model and memory

NVIDIA A100-SXM4-80GB

### Current behavior?

Whenever I use the cache function on my tf.data validation dataset I get the warning below. When I use the cache only and without the validation set, no warning appears.

### Standalone code to reproduce the issue

```shell
dataset = tf.data.Dataset.from_generator(pygen.generator, args=[files,minmax],output_signature=(
    tf.TensorSpec(shape=s[0], dtype=tf.float32),
    tf.TensorSpec(shape=s[1], dtype=tf.float32)))

    val_dataset = tf.data.Dataset.from_generator(pygen.generator, args=[val_files,minmax],output_signature=(
    tf.TensorSpec(shape=s[0], dtype=tf.float32),
    tf.TensorSpec(shape=s[1], dtype=tf.float32)))
dataset = dataset.take(len(files)).cache().batch(args.bs).repeat(args.epochs).prefetch(tf.data.AUTOTUNE) 
    
val_dataset = val_dataset.take(len(val_files)).cache(filename=f'{tempfile.gettempdir()}/val').batch(64).repeat(args.epochs).prefetch(tf.data.AUTOTUNE)
strategy = tf.distribute.MultiWorkerMirroredStrategy()
        with strategy.scope():
            m = unet28.build(s[0])
            m.fit(dataset,validation_data=val_dataset, epochs=args.ep, steps_per_epoch = spe,validation_steps = vspe,callbacks=[model_checkpoint_callback,save50,model_csv_logger,model_tensorboard,model_earlystopping_30],verbose=2)
```


### Relevant log output

```shell
2023-07-03 13:47:14.532355: W tensorflow/core/kernels/data/cache_dataset_ops.cc:296] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
```
