Dataset is never fully read when caching to disk
@vanskarner,
I tried to execute the mentioned code with **train_data.take(1) or train_data.take(1).cache()** on both tensorflow [v2.15](https://colab.research.google.com/gist/tilakrayal/53f5ab525c088b7fd7f959f8a32c5cf6/untitled1734.ipynb) and v2.13, and observed that the output is intended and also the image is also visible. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/a10ec69e60ef2fe3c67c02f3844e7669/untitled1735.ipynb) and let me know. Thank you! > @vanskarner, I tried to execute the mentioned code with **train_data.take(1) or train_data.take(1).cache()** on both tensorflow [v2.15](https://colab.research.google.com/gist/tilakrayal/53f5ab525c088b7fd7f959f8a32c5cf6/untitled1734.ipynb) and v2.13, and observed that the output is intended and also the image is also visible. Kindly find the gist of it [here](https://colab.research.google.com/gist/tilakrayal/a10ec69e60ef2fe3c67c02f3844e7669/untitled1735.ipynb) and let me know. Thank you!

Hi, when the code is executed in the google colab there are no resulting messages, however based on the template I filled in(OS platform and distribution) for this issue I still get the same message even if I upgrade to tensorflow v2.15, either using train_data.take(1) or train_data.take(1).cache() generate the same message described in Relevant log output. @vanskarner,
I suspect this is happening only with the Windows OS. When I tried to execute the same code on the other environments like Linux, MacOS and Colab, I don't face any issue/warning with the tensorflow 2.15.

Also the message is the Warning(W) which might not affect the execution of the code. 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?
<a href="https://docs.google.com/forms/d/e/1FAIpQLSfaP12TRhd9xSxjXZjcZFNXPGk4kc1-qMdv3gc6bEP90vY1ew/viewform?entry.85265664=Yes&entry.2137816233=https://github.com/tensorflow/tensorflow/issues/63019">Yes</a>
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### Issue type

Bug

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

No

### Source

source

### TensorFlow version

tf 2.13.1

### Custom code

Yes

### OS platform and distribution

Windows 11

### Mobile device

_No response_

### Python version

Python 3.9.13

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

When displaying the dataset elements for viewing with either `train_data.take(1)` or `train_data.take(1).cache()` it always shows the message "The calling iterator did not fully read the dataset being cached...".

The following has already been tried, but the same message always appears:

```python
# train_data = train_data.cache()
# train_data = train_data.take(1)
# train_data = train_data.take(1).cache()
# train_data = train_data.take(1).prefetch(tf.data.experimental.AUTOTUNE)
```
There is a similar problem but the bot closed it due to inactivity: #60174 


### Standalone code to reproduce the issue

```shell
import tensorflow_datasets as tfds
import matplotlib.pyplot as plt

data, metadata = tfds.load(
    name='fashion_mnist',
    as_supervised=True,
    with_info=True)
train_data = data['train']
categories = metadata.features['label'].names

plt.suptitle('First Item')
for image, label_index in train_data.take(1):
    plt.imshow(image, cmap='binary')
    plt.xlabel(categories[label_index])
    plt.colorbar()
    plt.grid(visible=False)
    plt.show()
```


### Relevant log output

```shell
2024-02-21 14:07:32.650452: W tensorflow/core/kernels/data/cache_dataset_ops.cc:854] 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.
```
