class_docstr: TODO(Mark): Add paper reference.
  
  Winobias is a dataset to analyse the issue of gender bias in co-reference
  resolution. It contains simple sentences with pro/anti stereotypical gender
  associations with which to measure the bias of a coreference system trained
  on another corpus. It is effectively a toy dataset and as such, uses very
  simplistic language; it has little use outside of evaluating a model for bias.
  
  The dataset is formatted with a single sentence per line, with a maximum of 2
  non-nested coreference clusters annotated using either square or round brackets.
  For example:
  
  [The salesperson] sold (some books) to the librarian because [she] was trying to sell (them).
  
  
  Returns a list of ``Instances`` which have four fields: ``text``, a ``TextField``
  containing the full sentence text, ``spans``, a ``ListField[SpanField]`` of inclusive start and
  end indices for span candidates, and ``metadata``, a ``MetadataField`` that stores the instance's
  original text. For data with gold cluster labels, we also include the original ``clusters``
  (a list of list of index pairs) and a ``SequenceLabelField`` of cluster ids for every span
  candidate in the ``metadata`` also.
  
  Parameters
  ----------
  max_span_width: ``int``, required.
      The maximum width of candidate spans to consider.
  token_indexers : ``Dict[str, TokenIndexer]``, optional
      This is used to index the words in the sentence.  See :class:`TokenIndexer`.
      Default is ``{"tokens": SingleIdTokenIndexer()}``.
code: def _read(self, file_path):
      for sentence in open(cached_path(file_path), "r"):
          tokens = sentence.strip().split(" ")
          clusters = collections.defaultdict(list)
          words = []
          for index, token in enumerate(tokens):
              # Coreference is annotated using [square brackets]
              # or (round brackets) around coreferent phrases.
              if "[" in token and "]" in token:
                  clusters[0].append((index, index))
              elif "[" in token:
                  clusters[0].append((index, index))
              elif "]" in token:
                  old_span = clusters[0][-1]
                  clusters[0][-1] = (old_span[0], index)
  
              if "(" in token and ")" in token:
                  clusters[1].append((index, index))
              elif "(" in token:
                  clusters[1].append((index, index))
              elif ")" in token:
                  old_span = clusters[1][-1]
                  clusters[1][-1] = (old_span[0], index)
  
              if token.endswith("."):
                  # Winobias is tokenised, but not for full stops.
                  # We'll just special case them here.
                  token = token[:-1]
                  words.append(token.strip("[]()"))
                  words.append(".")
              else:
                  words.append(token.strip("[]()"))
  
          yield self.text_to_instance(
              [Token(x) for x in words],
              [x for x in list(clusters.values())],
          )
  
file_path: magnitude/pymagnitude/third_party/allennlp/data/dataset_readers/coreference_resolution/winobias.py
imports: from __future__ import absolute_import
  import logging
  import collections
  from allennlp.common.file_utils import cached_path
  from allennlp.data.dataset_readers.dataset_reader import DatasetReader
  from allennlp.data.fields import Field, ListField, TextField, SpanField, MetadataField, SequenceLabelField
  from allennlp.data.instance import Instance
  from allennlp.data.tokenizers import Token
  from allennlp.data.token_indexers import SingleIdTokenIndexer, TokenIndexer
  from allennlp.data.dataset_readers.dataset_utils import enumerate_spans
  from io import open
label_desc: Use image node_id 2 for calling the WinobiasReader obj's underlying member method code with example usage: obj._read(file_path) without return types