You may have to google how RNN works but basically you have TIMExBATCHxN_FEATURES.
If you have a sample that sample can be size 100 or size 1000. It doesn’t mean that you split your timeseries into samples. Each sample is a totally different thing.
You are also forgetting that your sample may have different feature lengths, eg, a video is a HxW feature space, a sentence may be a onehotvector of Q elements.
Also, length T is not fixed for each sample, how may have a video of 10 seconds and another one of 4.
Practical examples,
you encode a word dictionary in a one hot vector of 100 elements. Thus your n_features is 100.
Now you have a book, each sentence has a different length.
for sentence one you may have 5 words --> 5x100
Sentence 2–> 9 words --> 9x100
and so on up to sentence 4
If you get a batch of sentences you will have Tx[S1…S4]x100 but T is different for each sample.
For a video you have have different lengths also.
You n_features can be HxW
Each video contains TxFPS frames
if you have 5 videos you would get [TxFPS,5,HxW] features