class_docstr: The implementation of Biaffine Dependency Parser.
  
  References:
      - Timothy Dozat and Christopher D. Manning. 2017.
        `Deep Biaffine Attention for Neural Dependency Parsing`_.
  
  .. _Deep Biaffine Attention for Neural Dependency Parsing:
      https://openreview.net/forum?id=Hk95PK9le
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docstr: Args:
      s_arc (~torch.Tensor): ``[batch_size, seq_len, seq_len]``.
          Scores of all possible arcs.
      s_rel (~torch.Tensor): ``[batch_size, seq_len, seq_len, n_labels]``.
          Scores of all possible labels on each arc.
      mask (~torch.BoolTensor): ``[batch_size, seq_len]``.
          The mask for covering the unpadded tokens.
      tree (bool):
          If ``True``, ensures to output well-formed trees. Default: ``False``.
      proj (bool):
          If ``True``, ensures to output projective trees. Default: ``False``.
  
  Returns:
      ~torch.Tensor, ~torch.Tensor:
          Predicted arcs and labels of shape ``[batch_size, seq_len]``.
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code: def decode(self, s_arc, s_rel, mask, tree=False, proj=False):
      r"""
      Args:
          s_arc (~torch.Tensor): ``[batch_size, seq_len, seq_len]``.
              Scores of all possible arcs.
          s_rel (~torch.Tensor): ``[batch_size, seq_len, seq_len, n_labels]``.
              Scores of all possible labels on each arc.
          mask (~torch.BoolTensor): ``[batch_size, seq_len]``.
              The mask for covering the unpadded tokens.
          tree (bool):
              If ``True``, ensures to output well-formed trees. Default: ``False``.
          proj (bool):
              If ``True``, ensures to output projective trees. Default: ``False``.
  
      Returns:
          ~torch.Tensor, ~torch.Tensor:
              Predicted arcs and labels of shape ``[batch_size, seq_len]``.
      """
  
      lens = mask.sum(1)
      arc_preds = s_arc.argmax(-1)
      bad = [
          not CoNLL.istree(seq[1 : i + 1], proj)
          for i, seq in zip(lens.tolist(), arc_preds.tolist())
      ]
      if tree and any(bad):
          alg = eisner if proj else mst
          arc_preds[bad] = alg(s_arc[bad], mask[bad])
      rel_preds = (
          s_rel.argmax(-1)
          .gather(-1, arc_preds.unsqueeze(-1))
          .squeeze(-1)
      )
  
      return arc_preds, rel_preds
  
imports: import os
  from datetime import datetime
  from underthesea.utils import logger
  from underthesea.utils.sp_data import Dataset
  from underthesea.utils.sp_field import Field
  from underthesea.utils.sp_fn import ispunct
  from underthesea.utils.sp_init import PRETRAINED
  from underthesea.utils.sp_metric import AttachmentMetric
  import torch
  import torch.nn
  from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
  from underthesea.transforms.conll import CoNLL
  from underthesea.modules.base import CharLSTM, IndependentDropout, BiLSTM, SharedDropout, MLP, Biaffine
  from underthesea.modules.bert import BertEmbedding
  from underthesea.utils.sp_alg import eisner, mst
  import underthesea.modules.nn
  from underthesea.utils.util_deep_learning import device
label_desc: Use image node_id 11 for calling the DependencyParser obj's underlying member method code with example usage: obj.decode(s_arc, s_rel, mask, tree, proj) and returns: arc_preds,  rel_preds