I have asked the question stated in the title on several GenAI chat and the LLama model gives me the best answer which was quite impressive as follows:

That’s a great question!

You’re right that TensorFlow’s tf.data API provides a shuffle method, but not a sort method. This might seem counterintuitive, but there’s a good reason for it.

The shuffle method is designed to randomly reorder the elements of a dataset, which is useful for various machine learning tasks, such as:

Randomizing the order of training data: Shuffling the dataset helps to reduce overfitting by presenting the model with a different order of examples in each epoch.
Creating random batches: Shuffling is essential for creating random batches of data, which is a common practice in deep learning.

The shuffle method is implemented in a way that is efficient and scalable, even for large datasets. It uses a buffer to store a subset of the dataset, and then randomly samples from this buffer to create the shuffled output.

On the other hand, sorting a dataset is a more complex operation that requires a different approach. Sorting requires a total order on the elements, which can be computationally expensive and may not be feasible for large datasets. Additionally, sorting can be sensitive to the specific implementation and may not be stable across different platforms or versions of TensorFlow.

By not providing a sort method, the TensorFlow team is encouraging users to think carefully about their use case and consider alternative approaches, such as:

Sorting outside of TensorFlow: If you need to sort your data, you can do so before creating a TensorFlow dataset, using NumPy or Pandas, for example.
Using a different data structure: Depending on your use case, you might be able to use a different data structure, such as a NumPy array or a Pandas DataFrame, which do provide sorting capabilities.

So, while it might seem inconsistent that tf.data provides a shuffle method but not a sort method, it’s a deliberate design choice that reflects the different use cases and requirements for these operations.

However when I further asked for grounding or resource which the answer was based on it did not know exactly. I also tried to find an official documentation or the source where the issue has been discussed but I had no success either. Can someone show me the tensorflow documentation for this?