class_docstr: Field dealing with constituency trees.
  
  This field receives sequences of binarized trees factorized in pre-order,
  and returns two tensors representing the bracketing trees and labels on each constituent respectively.
  
  Examples:
      >>> sequence = [(0, 5, 'S'), (0, 4, 'S|<>'), (0, 1, 'NP'), (1, 4, 'VP'), (1, 2, 'VP|<>'),
                      (2, 4, 'S+VP'), (2, 3, 'VP|<>'), (3, 4, 'NP'), (4, 5, 'S|<>')]
      >>> spans, labels = field.transform([sequence])[0]  # this example field is built from ptb
      >>> spans
      tensor([[False,  True, False, False,  True,  True],
              [False, False,  True, False,  True, False],
              [False, False, False,  True,  True, False],
              [False, False, False, False,  True, False],
              [False, False, False, False, False,  True],
              [False, False, False, False, False, False]])
      >>> labels
      tensor([[  0,  37,   0,   0, 107,  79],
              [  0,   0, 120,   0, 112,   0],
              [  0,   0,   0, 120,  86,   0],
              [  0,   0,   0,   0,  37,   0],
              [  0,   0,   0,   0,   0, 107],
              [  0,   0,   0,   0,   0,   0]])
class_docstr_tok: ["Field","dealing","with","constituency","trees",".","This","field","receives","sequences","of","binarized","trees","factorized","in","pre-order",",","and","returns","two","tensors","representing","the","bracketing","trees","and","labels","on","each","constituent","respectively",".","Examples",":",">",">",">","sequence","=","[","(","0",",","5",",","'S","'",")",",","(","0",",","4",",","'S|","<",">","'",")",",","(","0",",","1",",","'NP","'",")",",","(","1",",","4",",","'VP","'",")",",","(","1",",","2",",","'VP|","<",">","'",")",",","(","2",",","4",",","'S+VP","'",")",",","(","2",",","3",",","'VP|","<",">","'",")",",","(","3",",","4",",","'NP","'",")",",","(","4",",","5",",","'S|","<",">","'",")","]",">",">",">","spans",",","labels","=","field.transform","(","[","sequence","]",")","[","0","]","#","this","example","field","is","built","from","ptb",">",">",">","spans","tensor","(","[","[","False",",","True",",","False",",","False",",","True",",","True","]",",","[","False",",","False",",","True",",","False",",","True",",","False","]",",","[","False",",","False",",","False",",","True",",","True",",","False","]",",","[","False",",","False",",","False",",","False",",","True",",","False","]",",","[","False",",","False",",","False",",","False",",","False",",","True","]",",","[","False",",","False",",","False",",","False",",","False",",","False","]","]",")",">",">",">","labels","tensor","(","[","[","0",",","37",",","0",",","0",",","107",",","79","]",",","[","0",",","0",",","120",",","0",",","112",",","0","]",",","[","0",",","0",",","0",",","120",",","86",",","0","]",",","[","0",",","0",",","0",",","0",",","37",",","0","]",",","[","0",",","0",",","0",",","0",",","0",",","107","]",",","[","0",",","0",",","0",",","0",",","0",",","0","]","]",")"]
code: def compose(self, sequences):
      return [pad(i).to(self.device) for i in zip(*sequences)]
  
imports: from collections import Counter
  import torch
  from underthesea.utils.sp_fn import pad
  from underthesea.utils.sp_vocab import Vocab
label_desc: Use image node_id 3 for calling the ChartField obj's underlying member method code with example usage: obj.compose(sequences) and returns: unknown