TextVectorization layer vocabulary doesn't match custom standardization function
Hi @AndhikaWB ,

I am not getting your question here. I can verify that `TextVectorization(...,standardize = 'lower_and_strip_punctuation')` layer works fine which removed all punctuations and converted input into lower text as well as per attached [gist](https://colab.sandbox.google.com/gist/SuryanarayanaY/a612b8fae2e4d874290be2f7c8ead8f5/62653.ipynb). 

Where as the callable `custom_standardization` defined by you not able to remove the punctuations. Is that what you wanted to report ? In that case this is not an issue with Tensorflow. You need to verify the custom callable code correctly and if you are looking for such support please report the issue at [TF-Forum](https://discuss.tensorflow.org/) or **stack overflow** . 

If you are reporting any Tensorflow bug please let us know. Thanks! @SuryanarayanaY Yes, but I already checked that with debugging, the `custom_standardization` function that I wrote return the text correctly with punctuations and other useless stuff removed.

It should have the same behavior as Tensorflow built-in `lower_and_strip_punctuation`, but in this case, if I use the callable function (`custom_standardization`), Tensorflow will use the unprocessed vocabulary (output from `vectorization_layer.get_vocabulary()`), with punctuations and other useless stuff still intact.

1. Here is the return from Tensorflow `lower_and_strip_punctuation`:
![image](https://github.com/tensorflow/tensorflow/assets/17119394/a0354ec7-c4e4-48f3-8c9d-935561e9cc94)

    Here is the return from my `custom_standardization` function (I removed the `casefold`/`lowercase` temporarily so you can see the case difference, but punctuation removal is still active):
![image](https://github.com/tensorflow/tensorflow/assets/17119394/3f6b959d-ad00-4c2b-9c90-0e31f0428d64)

2. Here is the vocabulary list from Tensorflow `lower_and_strip_punctuation`:
![image](https://github.com/tensorflow/tensorflow/assets/17119394/ee308186-f341-4cc0-918c-73cd9d451b17)

    Here is the vocabulary list from my `custom_standardization` (note that the punctuations are still there):
![image](https://github.com/tensorflow/tensorflow/assets/17119394/1c551216-7d09-493a-99c4-046358ed733c)

As you can see from the `custom_standardization` vocabulary list (printed output), it still have punctuations, even though I already removed it before, see `return inputs` variable (`rude`) vs the vocabulary list (`rude!!!`). It's not that my code is wrong, it's just Tensorflow ignore my preprocessed words.
### Issue type

Bug

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

No

### Source

binary

### TensorFlow version

v2.15.0-rc1-8-g6887368d6d4 2.15.0

### Custom code

Yes

### OS platform and distribution

Windows 11 22621 x64

### Mobile device

_No response_

### Python version

3.11.5

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

When using custom `standardize` function in `TextVectorization` layer, vocabulary still match the unprocessed words. For example if the sentence contains punctuation or mixed case word like:

``` python
vectorization_layer = keras.layers.TextVectorization(
    max_tokens = 12000 + 2,
    standardize = custom_standardization
)

text = [
    'Your company is the best, especially the service you provided 🤩',
    'The customer service is so-so, not good but not bad either',
    'The service is NOT GOOD at all, the guy I contacted is so rude!!!'
]

vectorization_layer.adapt(text)
print('Vocabulary:', vectorization_layer.get_vocabulary(), '\n')
print('Output vector:', vectorization_layer(text))
```

The vocabulary list will be:
```
['', '[UNK]', 'is', 'the', 'service', 'not', 'The', '🤩', 'you', 'so-so,', 'so', 'rude!!!', 'provided', 'guy', 'good', 'especially', 'either', 'customer', 'contacted', 'company', 'but', 'best,', 'bad', 'at', 'all,', 'Your', 'NOT', 'I', 'GOOD']
```

But the custom standardization model is basically like this:
``` python
def preproc_for_model(value: str, stemmer = None, stop_words = None):
    # Remove punctuations (and emoticons)
    value = re.sub(f'[{string.punctuation}]', ' ', value)
    # Lowercase, but ignore unicode characters
    value = value.casefold()
    # Remove 1-character word
    value = re.sub(r'\b[a-zA-Z]\b', '', value)
    # Replace multiple spaces with a single space
    value = re.sub(r'\s+', ' ', value)

    if stemmer or stop_words:
        # Split sentence as words (tokenization)
        word_token = value.split()

        if stop_words:
            # Remove stop words (must be prioritized before stemmer)
            word_token = [ word for word in word_token if word not in stop_words ]
        if stemmer:
            # Stem/lemmatize words
            try: word_token = [ stemmer.lemmatize(word) for word in word_token ]
            except: word_token = [ stemmer.stem(word) for word in word_token ]

        # Join the tokenized words again
        # A bit inefficient if we use it with TextVectorization layer
        value = ' '.join(word_token)
    
    return value

def custom_standardization(input_data):
    # Skip if the layer is just initialized (no data yet)
    if tf.is_symbolic_tensor(input_data): return input_data

    # Convert tensor to Numpy's array (for easier manipulation)
    # The array will have byte as it's data type
    input_data = input_data.numpy()

    __preproc_for_model = np.vectorize(
        lambda sentence: bytes(
            # Please change to "preproc_all" instead for unseen data
            # Since we did sentiment analysis earlier, "preproc_useless_info" is already applied
            # So all that's left is to apply "preproc_for_model" (remove punctuations, etc)
            preproc_for_model(sentence.decode(encoding)),
            encoding
        ),
        cache = True
    )

    # Appply preprocessing and convert back Numpy's byte array to tensor
    input_data = __preproc_for_model(input_data)
    input_data = tf.convert_to_tensor(input_data)

    return input_data
```

Which also applies lowercase and remove punctuations.

This doesn't happen when I use the default parameter (`lower_and_strip_punctuation`). The vocabulary is lowered and the punctuations are removed properly:
```
['', '[UNK]', 'the', 'is', 'service', 'not', 'good', '🤩', 'your', 'you', 'soso', 'so', 'rude', 'provided', 'i', 'guy', 'especially', 'either', 'customer', 'contacted', 'company', 'but', 'best', 'bad', 'at', 'all']
```


### Standalone code to reproduce the issue

```shell
import tensorflow as tf
import keras

def preproc_for_model(value: str, stemmer = None, stop_words = None):
    # Remove punctuations (and emoticons)
    value = re.sub(f'[{string.punctuation}]', ' ', value)
    # Lowercase, but ignore unicode characters
    value = value.casefold()
    # Remove 1-character word
    value = re.sub(r'\b[a-zA-Z]\b', '', value)
    # Replace multiple spaces with a single space
    value = re.sub(r'\s+', ' ', value)

    if stemmer or stop_words:
        # Split sentence as words (tokenization)
        word_token = value.split()

        if stop_words:
            # Remove stop words (must be prioritized before stemmer)
            word_token = [ word for word in word_token if word not in stop_words ]
        if stemmer:
            # Stem/lemmatize words
            try: word_token = [ stemmer.lemmatize(word) for word in word_token ]
            except: word_token = [ stemmer.stem(word) for word in word_token ]

        # Join the tokenized words again
        # A bit inefficient if we use it with TextVectorization layer
        value = ' '.join(word_token)
    
    return value

# Max words/features/vocabulary to save
# Only the most frequent words will be saved
max_vocab = 12000
# Max words for each sentence
max_len = 50

# Encoding to interpret the text (to be passed to the model)
# Some encodings can remove "unique" characters such as emojis (e.g. latin-1)
encoding = 'utf-8'

# Standardization to apply before doing vectorization
# This is a hacky function to automate text preprocessing as part of the model
# There is no need to apply preprocessing separately (unless you use Tokenizer)
def custom_standardization(input_data):
    # Skip if the layer is just initialized (no data yet)
    if tf.is_symbolic_tensor(input_data): return input_data

    # Convert tensor to Numpy's array (for easier manipulation)
    # The array will have byte as it's data type
    input_data = input_data.numpy()

    """ # TODO Apply vectorization
    for i in range(len(input_data)):
        # Apply preprocessing
        # Convect byte to string and vice versa
        input_data[i] = bytes(
            preproc_for_model(input_data[i].decode('utf-8')),
            'utf-8'
        ) """

    __preproc_for_model = np.vectorize(
        lambda sentence: bytes(
            # Please change to "preproc_all" instead for unseen data
            # Since we did sentiment analysis earlier, "preproc_useless_info" is already applied
            # So all that's left is to apply "preproc_for_model" (remove punctuations, etc)
            preproc_for_model(sentence.decode(encoding)),
            encoding
        ),
        cache = True
    )

    # Appply preprocessing and convert back Numpy's byte array to tensor
    input_data = __preproc_for_model(input_data)
    input_data = tf.convert_to_tensor(input_data)

    return input_data

# https://www.tensorflow.org/api_docs/python/tf/keras/layers/TextVectorization
# https://www.tensorflow.org/guide/keras/preprocessing_layers
vectorization_layer = keras.layers.TextVectorization(
    # Low vocabulary may affect accuracy, while high vocabulary can affect complexity & memory usage
    # For comparison, the latest VADER (v3.3.2) has 11090 vocabulary (combined with emojis)
    # Note that 2 spaces will be reserved to for empty word and OOV (out-of-vocabulary)
    max_tokens = max_vocab + 2,
    # "lower_and_strip_punctuation", "lower", "strip_punctuation", None, or custom function
    # FIXME Currently, the custom function won't change the output (stopwords not removed, etc)
    standardize = custom_standardization,
    # Use 2 or higher to possibly detect idioms, negation, slang words, etc
    # None means no N-grams when splitting words aka 1-gram
    ngrams = None,
    # Either "int" (index array), "multi_hot" (0/1 array), "count" (count array), or "tf_idf" (ratio array)
    # Use "int" to minimize array size, but may be a burden for computation later (e.g. multiplication)
    output_mode = 'int',
    # Apply post-padding (all zeros) to each array up to max tokens, ignored if using "int" mode
    # It's like using the "pad_sequences" function, though there is no pre-padding
    # Regardless it's used or not, all arrays will have the same length
    pad_to_max_tokens = True,
    # Limit array size for each sample, only applies to "int" mode
    # Useful to further reduce memory usage and limit garbage words, but can affect accuracy
    # If the array size is less than the specified length, post-padding will be applied
    output_sequence_length = None,
    # Encoding to interpret the text
    # Some encodings can remove "unique" characters such as emojis (e.g. latin-1)
    encoding = encoding,
    # Layer name (optional)
    name = 'text_vectorization'
)

text = [
    'Your company is the best, especially the service you provided 🤩',
    'The customer service is so-so, not good but not bad either',
    'The service is NOT GOOD at all, the guy I contacted is so rude!!!'
]

# Experiment with the hyperparameters above, then test the result
vectorization_layer.adapt(text)
print('Vocabulary:', vectorization_layer.get_vocabulary(), '\n')
print('Output vector:', vectorization_layer(text))
```


### Relevant log output

_No response_