class_docstr: This dependency parser follows the model of
  ` Deep Biaffine Attention for Neural Dependency Parsing (Dozat and Manning, 2016)
  <https://arxiv.org/abs/1611.01734>`_ .
  
  Word representations are generated using a bidirectional LSTM,
  followed by separate biaffine classifiers for pairs of words,
  predicting whether a directed arc exists between the two words
  and the dependency label the arc should have. Decoding can either
  be done greedily, or the optimial Minimum Spanning Tree can be
  decoded using Edmond's algorithm by viewing the dependency tree as
  a MST on a fully connected graph, where nodes are words and edges
  are scored dependency arcs.
  
  Parameters
  ----------
  vocab : ``Vocabulary``, required
      A Vocabulary, required in order to compute sizes for input/output projections.
  text_field_embedder : ``TextFieldEmbedder``, required
      Used to embed the ``tokens`` ``TextField`` we get as input to the model.
  encoder : ``Seq2SeqEncoder``
      The encoder (with its own internal stacking) that we will use to generate representations
      of tokens.
  tag_representation_dim : ``int``, required.
      The dimension of the MLPs used for dependency tag prediction.
  arc_representation_dim : ``int``, required.
      The dimension of the MLPs used for head arc prediction.
  tag_feedforward : ``FeedForward``, optional, (default = None).
      The feedforward network used to produce tag representations.
      By default, a 1 layer feedforward network with an elu activation is used.
  arc_feedforward : ``FeedForward``, optional, (default = None).
      The feedforward network used to produce arc representations.
      By default, a 1 layer feedforward network with an elu activation is used.
  pos_tag_embedding : ``Embedding``, optional.
      Used to embed the ``pos_tags`` ``SequenceLabelField`` we get as input to the model.
  use_mst_decoding_for_validation : ``bool``, optional (default = True).
      Whether to use Edmond's algorithm to find the optimal minimum spanning tree during validation.
      If false, decoding is greedy.
  dropout : ``float``, optional, (default = 0.0)
      The variational dropout applied to the output of the encoder and MLP layers.
  input_dropout : ``float``, optional, (default = 0.0)
      The dropout applied to the embedded text input.
  initializer : ``InitializerApplicator``, optional (default=``InitializerApplicator()``)
      Used to initialize the model parameters.
  regularizer : ``RegularizerApplicator``, optional (default=``None``)
      If provided, will be used to calculate the regularization penalty during training.
code: def get_metrics(self, reset=False):
      return self._attachment_scores.get_metric(reset)
  
imports: from __future__ import division
  from __future__ import absolute_import
  import logging
  import copy
  import torch
  import torch.nn.functional
  from torch.nn.modules import Dropout
  import numpy
  from allennlp.common.checks import check_dimensions_match, ConfigurationError
  from allennlp.data import Vocabulary
  from allennlp.modules import Seq2SeqEncoder, TextFieldEmbedder, Embedding, InputVariationalDropout
  from allennlp.modules.matrix_attention.bilinear_matrix_attention import BilinearMatrixAttention
  from allennlp.modules import FeedForward
  from allennlp.models.model import Model
  from allennlp.nn import InitializerApplicator, RegularizerApplicator, Activation
  from allennlp.nn.util import get_text_field_mask, get_range_vector
  from allennlp.nn.util import get_device_of, last_dim_log_softmax, get_lengths_from_binary_sequence_mask
  from allennlp.nn.decoding.chu_liu_edmonds import decode_mst
  from allennlp.training.metrics import AttachmentScores
label_desc: Use image node_id 10 for calling the BiaffineDependencyParser obj's underlying member method code with example usage: obj.get_metrics(reset) and returns: self