class_docstr: This module uses the "forward-backward" algorithm to compute
  the log-likelihood of its inputs assuming a conditional random field model.
  
  See, e.g. http://www.cs.columbia.edu/~mcollins/fb.pdf
  
  Parameters
  ----------
  num_tags : int, required
      The number of tags.
  constraints : List[Tuple[int, int]], optional (default: None)
      An optional list of allowed transitions (from_tag_id, to_tag_id).
      These are applied to ``viterbi_tags()`` but do not affect ``forward()``.
      These should be derived from `allowed_transitions` so that the
      start and end transitions are handled correctly for your tag type.
  include_start_end_transitions : bool, optional (default: True)
      Whether to include the start and end transition parameters.
code: def reset_parameters(self):
      torch.nn.init.xavier_normal_(self.transitions)
      if self.include_start_end_transitions:
          torch.nn.init.normal_(self.start_transitions)
          torch.nn.init.normal_(self.end_transitions)
  
file_path: magnitude/pymagnitude/third_party/allennlp/modules/conditional_random_field.py
imports: from __future__ import absolute_import
  import torch
  from allennlp.common.checks import ConfigurationError
  import allennlp.nn.util
label_desc: Use image node_id 2 for calling the ConditionalRandomField obj's underlying member method code with example usage: obj.reset_parameters() without return types