docstr: Can choose to either handle or ignore variable length sequences.
  Always handle padding in eval.
docstr_tok: ["Can","choose","to","either","handle","or","ignore","variable","length","sequences",".","Always","handle","padding","in","eval","."]
code: def forward(self, x, x_mask):
      """Can choose to either handle or ignore variable length sequences.
      Always handle padding in eval.
      """
      # No padding necessary.
      if x_mask.data.sum() == 0:
          return self._forward_unpadded(x, x_mask)
      # Pad if we care or if its during eval.
      # if self.padding or not self.training:
      #    return self._forward_padded(x, x_mask)
      # We don't care.
      return self._forward_unpadded(x, x_mask)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.autograd import Variable
  import sru
label_desc: Use image node_id 2 for calling the StackedBRNN obj's underlying member method code with example usage: obj.forward(x, x_mask) and returns: self, self