code: def forward(self, seq, mask):
      # seq: (N, C, L)
      # mask: (N, L)
      max_len = seq.size()[2]
      length = get_length(mask)
      seq = torch.transpose(seq, 1, 2)  # to (N, L, C)
      packed_seq = nn.utils.rnn.pack_padded_sequence(
          seq, length, batch_first=True, enforce_sorted=False
      )
      outputs, _ = self.bid_rnn(packed_seq)
      outputs = nn.utils.rnn.pad_packed_sequence(
          outputs, batch_first=True, total_length=max_len
      )[0]
      outputs = outputs.view(-1, max_len, 2, self.hidden_size).sum(
          2
      )  # (N, L, C)
      outputs = self.out_dropout(outputs)  # output dropout
      return torch.transpose(outputs, 1, 2)
  
imports: import torch
  import torch.nn.functional
  from torch import nn
  from utils import get_length, INF
label_desc: Use image node_id 2 for calling the RNN obj's underlying member method code with example usage: obj.forward(seq, mask) and returns: torch