Given tf.train.Example, how to get tfds.features?

What I need help with / What I was wondering
Hi, I am trying to load external tfrecord files with TFDS. I have read the official doc here, and find I need to define the feature structure using tfds.features. However, since the tfrecords files are alreay generated, I do not have control the generation pipeline. I do, however, know the tf.train.Example structre used in TFRecordWriter during generation, shown as follows.

from tensorflow.python.training.training import BytesList, Example, Feature, Features, Int64List
dict(Example=Features({
'image': Feature(bytes_list=BytesList(value=[img_str])), # img_str is jpg encoded image raw bytes
'caption': Feature(bytes_list=BytesList(value=[caption])), # caption is a string
'height': Feature(bytes_list=Int64List(value=[caption])),
'width': Feature(bytes_list=Int64List(value=[caption])),
})

The doc only describes how to translate tfds.features into the human readable structure of the tf.train.Example. But nowhere does it mention how to translate a tf.train.Example into tfds.features, which is needed to automatically add the proper metadata fileswith tfds.folder_dataset.write_metadata.

I wonder how to translate the above tf.train.Example into tfds.features? Thanks a lot!

BTW, while I understand that it is possible to directly read the data as it is in TFRecord with tf.data.TFRecordDataset and then use map(decode_fn) for decoding as suggested here, it seems to me this approach lacks necessary metadata like num_shards or shard_lengths. In this case, I am not sure if it is still ok to use common operations like cache/repeat/shuffle/map/batch on that tf.data.TFRecordDataset. So I think it is better to stick to the tfds approach.

What I've tried so far
I have searched the official doc for quite some time but cannot find the answer. There is a Scalar class in tfds.features, which I assume could be used to decode Int64List. But How can I decode the BytesList

It would be nice if...
It would be nice if the the official doc here gives more description when the user don't control the generation pipeline, which is often the case. Because if the user does control the generation pipeline, the user can simply turn to the custom datasets writing doc here

Environment information

• tensorflow-datasets version: 4.8.2
• tensorflow version: 2.11.0

Start with the external TFRecord documentation and the tfds.folder_dataset.write_metadata documentation linked in the issue, then compare their guidance with the described tf.train.Example structure. Done means the official documentation clearly explains what can be inferred from an existing Example, how BytesList and Int64List map to TFDS features, and how metadata considerations affect this workflow.