Confused Regarding PyTorch GRU Docs
This may be too basic of a question, but what do the docs mean by the input to the GRU needs to be 3 dimensional? The GRU docs for PyTorch state:

input of shape (seq_len, batch, input_size): tensor containing the
features of the input sequence. The input can also be a packed
variable length sequence. See
torch.nn.utils.rnn.pack_padded_sequence() for details.

https://pytorch.org/docs/stable/generated/torch.nn.GRU.html
Let us say I am trying to predict the next # in a sequence and have the following dataset:
n, label
1, 2
2, 3
3, 6
4, 9
...

If I window the data using the prior 2 inputs for consideration when guessing the next, the dataset becomes:
t-2, t-1, t, label
na, na, 1, 2
na, 1, 2, 3
1, 2, 3, 6
2, 3, 4, 10
...

where t-x just represents using an input value from a prior time step.
So, when creating a sequential loader, it should create the following tensor for the line 1,2,3,6:
inputs: tensor([[1,2,3]]) #shape(1,3)
labels: tensor([[6]])     #shape(1,1)

I currently understand the input shape as (# batches, # features per batch)
and the output shape as (# batches, # output features per batch)
My question is, should that input tensor actually look like:
tensor([[[1],[2],[3]]])

Which represents (# batches, #prior inputs to consider, #features per input)
I guess I am better trying to understand why the input to a GRU has 3 dimensions in PyTorch. What does that 3rd dimension fundamentally represent? And if I have a transformed dataset like above, how to properly pass it to the model.
Edit:
So the pattern present is:
1 + 1 = 2
2 + 1 = 3
3 + 2 + 1 = 6
4+ 3 + 2 + 1 = 10

I want it where t-2, t-1, and t represent the features at each time step used to help guess. For example, at every point in time there could be 2 features. The dimensions would be (1 batch size, 3 timesteps, 2 features).
My question is wether the GRU takes a flattened input:
(1 batch size, 3 time steps * 2 features per time step)

or the unflattened input:
(1 batch size, 3 time steps, 2 features per timestep)

I am currently under the impression that it is the 2nd input, but would like to check my understanding.

I figured it out. Essentially, the sequence length of 3 means that the input to the system needs to be: [[[1],[2],[3]], [[2], [3], [4]]] for a batch size of 2, sequence length of 3, and feature input per time step of 1. Essentially each sequence is an input at some time t to consider.
