nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, bidirectional=bidirectional)

This is a PyTorch module that implements a Long Short-Term Memory (LSTM) layer. An LSTM is a type of recurrent neural network (RNN) that is designed to handle sequence data by maintaining a memory cell that can selectively forget or remember information over time.

The arguments for this module are:
- input_size: the number of expected features in the input
- hidden_size: the number of features in the hidden state/output
- num_layers: the number of recurrent layers (default is 1)
- bidirectional: if True, the LSTM will be bidirectional (default is False)

The input to this module is a tensor of shape (seq_len, batch, input_size), where seq_len is the length of the input sequence, batch is the batch size, and input_size is the number of expected features in the input.

The output of this module is a tensor of shape (seq_len, batch, num_directions * hidden_size), where num_directions is 2 if bidirectional=True, and 1 otherwise. The output represents the hidden state/output value for each element in the input sequence. The final hidden state is also returned as a tensor of shape (num_layers * num_directions, batch, hidden_size).