class_docstr: Given sequences X and Y, match sequence Y to each element in X.
  * o_i = sum(alpha_j * y_j) for i in X
  * alpha_j = softmax(y_j * x_i)
code: def __init__(self, input_size, identity=False):
      super(SeqAttnMatch, self).__init__()
      if not identity:
          self.linear = nn.Linear(input_size, input_size)
      else:
          self.linear = None
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.autograd import Variable
  import sru
label_desc: Use image node_id 1 to create a new SeqAttnMatch object from inherited base classes: nn with example: obj = SeqAttnMatch(input_size, identity)