class_docstr: Computes labeled and unlabeled attachment scores for a
  dependency parse, as well as sentence level exact match
  for both labeled and unlabeled trees. Note that the input
  to this metric is the sampled predictions, not the distribution
  itself.
  
  # Parameters
  
  ignore_classes : `List[int]`, optional (default = `None`)
      A list of label ids to ignore when computing metrics.
code: def reset(self):
      self._labeled_correct = 0.0
      self._unlabeled_correct = 0.0
      self._exact_labeled_correct = 0.0
      self._exact_unlabeled_correct = 0.0
      self._total_words = 0.0
      self._total_sentences = 0.0
  
file_path: allennlp/allennlp/training/metrics/attachment_scores.py
imports: from typing import Optional, List
  import torch
  from allennlp.nn.util import dist_reduce_sum
  from allennlp.training.metrics.metric import Metric
label_desc: Use image node_id 4 for calling the AttachmentScores obj's underlying member method code with example usage: obj.reset() without return types