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
class_docstr_tok: ["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"]
code: def evaluate(self, loader):
      self.eval()
  
      total_loss, metric = 0, AttachmentMetric()
  
      tree = self.args["tree"]
      proj = self.args["proj"]
  
      for words, feats, arcs, rels in loader:
          mask = words.ne(self.WORD.pad_index)
          # ignore the first token of each sentence
          mask[:, 0] = 0
          s_arc, s_rel = self.forward(words, feats)
          loss = self.forward_loss(s_arc, s_rel, arcs, rels, mask)
          arc_preds, rel_preds = self.decode(
              s_arc, s_rel, mask, tree, proj
          )
          # ignore all punctuation if not specified
          if not self.args["punct"]:
              mask &= words.unsqueeze(-1).ne(self.puncts).all(-1)
          total_loss += loss.item()
          metric(arc_preds, rel_preds, arcs, rels, mask)
      total_loss /= len(loader)
  
      return total_loss, metric
  
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 4 for calling the DependencyParser obj's underlying member method code with example usage: obj.evaluate(loader) and returns: total_loss,  metric