Hi there,  I’m new to pytroch (and the community!).
Sorry in advance if this is a silly question but as I’m getting my feet wet with LSTMs and learn pytorch at the same time I’m confused about how nn.LSTM ingests its inputs. From the main pytorch tutorial and the  time sequence prediction example 787 it looks like the input for an LSTM is a 3 dimensional vector, but I cannot understand why.
At the end of this thread it is mentioned that the three elements of the input are time dimension (5), feature dimension (3) and mini-batch dimension (100). The suggested 3d tensor is 5 x 100 x 3, which corresponds to time x batch x features.
I’m having trouble understanding this.
If I have a single time series of length 10000, but use mini batches of, say, length 50, and have no feature inputs other than the series itself, would this translate to a ? x 50 x 1 tensor?
What is referred as “time” in the thread above?
Is the batch size the size of the sliding window of observations that I will use to predict the “next” observed datapoint?
I think related to my misunderstanding is the fact that the time sequence prediction example 787 seems to be predicting multiple sine waves for each point in time, and in this thread @spro says that “any kind of continued sequence prediction counts as many-to-many”, which just adds to my confusion.
I feel my questions all stem from a misunderstanding of something very basic and connected.
Thanks for any help.