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.
docstr: Computes the numerator term for the log-likelihood, which is just score(inputs, tags)
docstr_tok: ["Computes","the","numerator","term","for","the","log-likelihood",",","which","is","just","score","(","inputs",",","tags",")"]
code: def _joint_likelihood(self, logits, tags, mask):
      """
      Computes the numerator term for the log-likelihood, which is just score(inputs, tags)
      """
      batch_size, sequence_length, num_tags = logits.data.shape
  
      # Transpose batch size and sequence dimensions:
      logits = logits.transpose(0, 1).contiguous()
      mask = mask.float().transpose(0, 1).contiguous()
      tags = tags.transpose(0, 1).contiguous()
  
      # Start with the transition scores from start_tag to the first tag in each input
      if self.include_start_end_transitions:
          score = self.start_transitions.index_select(0, tags[0])
      else:
          score = 0.0
  
      # Broadcast the transition scores to one per batch element
      broadcast_transitions = self.transitions.view(
          1, num_tags, num_tags
      ).expand(batch_size, num_tags, num_tags)
  
      # Add up the scores for the observed transitions and all the inputs but the last
      for i in range(sequence_length - 1):
          # Each is shape (batch_size,)
          current_tag, next_tag = tags[i], tags[i + 1]
  
          # The scores for transitioning from current_tag to next_tag
          transition_score = (
              broadcast_transitions
              # Choose the current_tag-th row for each input
              .gather(
                  1,
                  current_tag.view(batch_size, 1, 1).expand(
                      batch_size, 1, num_tags
                  ),
              )
              # Squeeze down to (batch_size, num_tags)
              .squeeze(1)
              # Then choose the next_tag-th column for each of those
              .gather(1, next_tag.view(batch_size, 1))
              # And squeeze down to (batch_size,)
              .squeeze(1)
          )
  
          # The score for using current_tag
          emit_score = (
              logits[i]
              .gather(1, current_tag.view(batch_size, 1))
              .squeeze(1)
          )
  
          # Include transition score if next element is unmasked,
          # input_score if this element is unmasked.
          score = (
              score
              + transition_score * mask[i + 1]
              + emit_score * mask[i]
          )
  
      # Transition from last state to "stop" state. To start with, we need to find the last tag
      # for each instance.
      last_tag_index = mask.sum(0).long() - 1
      last_tags = tags.gather(
          0,
          last_tag_index.view(1, batch_size).expand(
              sequence_length, batch_size
          ),
      )
  
      # Is (sequence_length, batch_size), but all the columns are the same, so take the first.
      last_tags = last_tags[0]
  
      # Compute score of transitioning to `stop_tag` from each "last tag".
      if self.include_start_end_transitions:
          last_transition_score = self.end_transitions.index_select(
              0, last_tags
          )
      else:
          last_transition_score = 0.0
  
      # Add the last input if it's not masked.
      last_inputs = logits[-1]  # (batch_size, num_tags)
      last_input_score = last_inputs.gather(
          1, last_tags.view(-1, 1)
      )  # (batch_size, 1)
      last_input_score = last_input_score.squeeze()  # (batch_size,)
  
      score = (
          score + last_transition_score + last_input_score * mask[-1]
      )
  
      return score
  
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 4 for calling the ConditionalRandomField obj's underlying member method code with example usage: obj._joint_likelihood(logits, tags, mask) and returns: score