I have a data sequence a which is of shape [seq_len, 2], seq_len is the length of the sequence. There is time correlation among elements of a[:, 0] and a[:, 1], but a[:, 0] and a[:, 1] are independent of each other. For training I prepare data of shape [batch_size, seq_len, 2]. The initialization of BRNN that I use is
birnn_layer = nn.RNN(input_size=2, hidden_size=100, batch_first=True, bidirectional=True)

From the docs 1,

input_size – The number of expected features in the input x


hidden_size – The number of features in the hidden state h

What does “number of expected features” mean? Since there is correlation along the seq_len axis should my input_size be set as seq_len and the input be permuted? Thanks.