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
  
  This is a weighted version of `ConditionalRandomField` which accepts a `label_weights`
  parameter to be used in the loss function in order to give different weights for each
  token depending on its label. The method implemented here is based on the paper
  *Weighted conditional random fields for supervised interpatient heartbeat
  classification* proposed by De Lannoy et. al (2019).
  See https://perso.uclouvain.be/michel.verleysen/papers/ieeetbe12gdl.pdf for more details.
  
  There are two other sample weighting methods implemented. You can find more details
  about them in: https://eraldoluis.github.io/2022/05/10/weighted-crf.html
  
  # Parameters
  
  num_tags : `int`, required
      The number of tags.
  label_weights : `List[float]`, required
      A list of weights to be used in the loss function in order to
      give different weights for each token depending on its label.
      `len(label_weights)` must be equal to `num_tags`. This is useful to
      deal with highly unbalanced datasets. The method implemented here was based on
      the paper *Weighted conditional random fields for supervised interpatient heartbeat
      classification* proposed by De Lannoy et. al (2019).
      See https://perso.uclouvain.be/michel.verleysen/papers/ieeetbe12gdl.pdf
  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)
code: def _joint_likelihood_lannoy(
      self,
      logits: torch.Tensor,
      tags: torch.Tensor,
      mask: torch.BoolTensor,
  ) -> torch.Tensor:
      """
      Computes the numerator term for the log-likelihood, which is just score(inputs, tags)
      """
      batch_size, sequence_length, _ = logits.data.shape
  
      # Transpose batch size and sequence dimensions:
      logits = logits.transpose(0, 1).contiguous()
      mask = mask.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
  
      label_weights = self.label_weights
  
      # weight transition score using current_tag, i.e., t(i,j) will be t(i,j)*w(i),
      # where t(i,j) is the score to transition from i to j and w(i) is the weight
      # for tag i.
      transitions = self.transitions * label_weights.view(-1, 1)
  
      # 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 = transitions[
              current_tag.view(-1), next_tag.view(-1)
          ]
  
          # The score for using current_tag
          emit_score = (
              logits[i]
              .gather(1, current_tag.view(batch_size, 1))
              .squeeze(1)
          )
  
          # Weight emit scores by label.
          emit_score *= label_weights[current_tag.view(-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)
      ).squeeze(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,)
  
      # Weight last emit scores by label weights.
      last_input_score = (
          last_input_score * label_weights[last_tags.view(-1)]
      )
  
      score = (
          score + last_transition_score + last_input_score * mask[-1]
      )
  
      return score
  
file_path: allennlp/allennlp/modules/conditional_random_field/conditional_random_field_wlannoy.py
imports: from typing import List, Tuple
  import torch
  from allennlp.common.checks import ConfigurationError
  from allennlp.modules.conditional_random_field.conditional_random_field import ConditionalRandomField
label_desc: Use image node_id 4 for calling the ConditionalRandomFieldWeightLannoy obj's underlying member method code with example usage: obj._joint_likelihood_lannoy(logits, tags, mask) and returns: score