Tensorflow official guide for optimizing pipeline performance in Data input pipelines does not include proper use cases
@AkshayRoyal Thank you for raising this issue!
Could you please have a look at [this](https://www.tensorflow.org/guide/data) link which explains about the large input data and let us know if it helps? Please share the complete standalone code to replicate the [issue](https://colab.research.google.com/gist/sushreebarsa/2c68de0312bacd525d5fe15301916feb/61167.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

Others

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

No

### Source

source

### TensorFlow version

v2.12.0-rc1-12-g0db597d0d75 2.12.0

### Custom code

Yes

### OS platform and distribution

RHEL8 .8

### Mobile device

_No response_

### Python version

3.10.11

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

The original tensorflow guide on [optimizing pipeline performance] (https://www.tensorflow.org/guide/data_performance#vectorizing_mapping) does not include useful use cases.

The function that is used to demonstrate the effectiveness of various techniques like batching , caching, mapping etc. is

```
def mapped_function(s):
    # Do some hard pre-processing
    tf.py_function(lambda: time.sleep(0.03), [], ())
    return s
```
This function is insensitive to a single element or a vector. How should we modify the function in order to deal with a batch, the case when we first batch the dataset and then map it with this function? Do we need to modify it in the first place or does tensorflow handle it on its own? These questions should have been answered in the guide.

I tried vectorized mapping( dataset.batch().map()) with this function

```
def function(x):
    return x+1

def tf_func(x):
    ans = tf.py_function(function, inp=x, Tout=['int64'])
    return ans

```

This also works fine as even in this case the function is be default capable of handling the vectors

but if we try a realistic scenario like this one

```
def read_files(filename):
    ...
    file = tf.io.read_file(filename)
    ...
```

This function fails to work throwing error that tf.io.read_file cannot handle the vector.

I think it would be helpful for all if practical use case scenarios are shown in the guide.


Thanks

### Standalone code to reproduce the issue

```shell
dataset = tf.data.Dataset.list_files(...) #glob and read filenames

def preprocess_files(filename): #read and pre-process the files here
    ...
    file = tf.io.read_file(filename) #this function throws error
    ...
```


### Relevant log output

_No response_