Why doesn't `The calling iterator did not fully read the dataset being cached.` appear on Google Colab?
Hi @miticollo ,

Colab usually filters Info and Warning logs. That's why you are not able to see it. The warning descriptions states if you are caching and then applying take() or batch() method. If your dataset is large caching the whole dataset might be a problem and can leads to truncation of dataset. This is just a warning to inform the user about possible data truncation.

Since tf.data.Dataset is a pipeline object you can first batching the dataset and then caching.

```
dataset = tf.data.Dataset.from_tensor_slices((X_valid, y_valid))
dataset = dataset.map(lambda x, y: (x, y), num_parallel_calls=tf.data.AUTOTUNE)
dataset = dataset.batch(32)
dataset = dataset.cache()
dataset = dataset.prefetch(buffer_size=tf.data.AUTOTUNE)
dataset = dataset.apply(tf.data.experimental.prefetch_to_device(device="/gpu:0"))
``` I tried to reduce dataset to 64 bytes and 70 bytes (I hope I did it correctly).
Then I run the notebook different times and I noticed that the warning sometimes appears and sometimes not. 
I don't know why, could be an issue?

Anyway these are outputs from 6 **sequential** runs (every time I turned off and then on the IPython runtime):
- [1](https://colab.research.google.com/drive/10jdLHyeteRgy8QhRT2VLtH00qBWyY8ZA?usp=sharing): the warning appears twice.
- [2](https://colab.research.google.com/drive/1MMNejSvhORnsG6pm71LbuWwbKwcR9mpU?usp=sharing): the warning appears only in the second case.
- [3](https://colab.research.google.com/drive/19y-rRfu3R5ehWXgliYuxKGxbZYMqybAq?usp=sharing): see case 2.
- [4](https://colab.research.google.com/drive/1FJSWWAdb2AK5oVdKCkVjmouUJBqcMyFE?usp=sharing): see case 2.
- [5](https://colab.research.google.com/drive/1Lo_XIqAsgiyZorDXTlCXWXwC3dLm_vGa?usp=sharing): see case 2.
- [6](https://colab.research.google.com/drive/1Jqja1rHOuDZ5IXwYkGSkhzlIHlhDHDAF?usp=sharing): see case 1. It seems to working as intended. Cache() operator will collect the data from upstream into an in-memory cache as the downstream iterator is pulling the data elements. As the user iterates through the full dataset, all of it will get processed and loaded into the in-memory cache.

The Colab version is running in eager mode and is waiting on further user inputs while holding the partial dataset in the cache. When running the notebook in a shell, it is evaluating the full session and given that the code only asks for a single element, it hasn't iterated through the full dataset. The warning is just informing the user that the cached data (i.e. data read so far will get discarded) and the iterators reset to the beginning.

If you were to replace:
```
x_batch, y_batch = next(iter(dataset))
```
with:
```
for (x_batch, y_batch) in dataset:
  pass
```
You shouldn't see this warning in the shell execution.

### Issue type

Bug

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

No

### Source

binary

### TensorFlow version

v2.16.1-0-g5bc9d26649c 2.16.1

### Custom code

Yes

### OS platform and distribution

Linux Manjaro

### Mobile device

_No response_

### Python version

3.11.8

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

CUDA Version: 12.4 (from `optirun nvidia-smi`)

### GPU model and memory

_No response_

### Current behavior?

[Here](https://colab.research.google.com/drive/1f3E2HyZ4bBNx-EHOxi3cLoIBBRdVCZZz?usp=sharing) the output running the notebook on my laptop.
Below you can find another Colab link that shows that the warning (see **Relevant log output**) doesn't appear on Google Colab.

I don't know if this is an issue. Maybe my laptop is not quite powerful or maybe the culprit is the Python version: Colab uses Python 3.10 while I tested the notebook on Python 3.11.8.

### Standalone code to reproduce the issue

```shell
https://colab.research.google.com/drive/1Ns5P1aPDcFjuoPohSDO567rPL1jluSun?usp=sharing
```


### Relevant log output

```shell
W tensorflow/core/kernels/data/cache_dataset_ops.cc:858] 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.
```
