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 log likelihood for the given batch of input sequences $(x,y)$
  
  Args:
      inputs (torch.Tensor): (batch_size, sequence_length, num_tags) tensor of logits for the inputs $x$
      tags (torch.Tensor): (batch_size, sequence_length) tensor of tags $y$
      mask (torch.BoolTensor, optional): (batch_size, sequence_length) tensor of masking flags.
          Defaults to None.
  
  Returns:
      torch.Tensor: (batch_size,) log likelihoods $log P(y|x)$ for each input
code: def forward(
      self,
      inputs: torch.Tensor,
      tags: torch.Tensor,
      mask: torch.BoolTensor = None,
  ) -> torch.Tensor:
      """Computes the log likelihood for the given batch of input sequences $(x,y)$
  
      Args:
          inputs (torch.Tensor): (batch_size, sequence_length, num_tags) tensor of logits for the inputs $x$
          tags (torch.Tensor): (batch_size, sequence_length) tensor of tags $y$
          mask (torch.BoolTensor, optional): (batch_size, sequence_length) tensor of masking flags.
              Defaults to None.
  
      Returns:
          torch.Tensor: (batch_size,) log likelihoods $log P(y|x)$ for each input
      """
      if mask is None:
          mask = torch.ones(
              *tags.size(), dtype=torch.bool, device=inputs.device
          )
      else:
          # The code below fails in weird ways if this isn't a bool tensor, so we make sure.
          mask = mask.to(torch.bool)
  
      log_denominator = self._input_likelihood_lannoy(
          inputs, tags, mask
      )
      log_numerator = self._joint_likelihood_lannoy(inputs, tags, mask)
  
      return torch.sum(log_numerator - log_denominator)
  
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 2 for calling the ConditionalRandomFieldWeightLannoy obj's underlying member method code with example usage: obj.forward(inputs, tags, mask) and returns: torch