class_docstr: Conditional random field.
  This module implements a conditional random field [LMP01]_. The forward computation
  of this class computes the log likelihood of the given sequence of tags and
  emission score tensor. This class also has `~CRF.decode` method which finds
  the best tag sequence given an emission score tensor using `Viterbi algorithm`_.
  Args:
      num_tags: Number of tags.
      batch_first: Whether the first dimension corresponds to the size of a minibatch.
  Attributes:
      start_transitions (`~torch.nn.Parameter`): Start transition score tensor of size
          ``(num_tags,)``.
      end_transitions (`~torch.nn.Parameter`): End transition score tensor of size
          ``(num_tags,)``.
      transitions (`~torch.nn.Parameter`): Transition score tensor of size
          ``(num_tags, num_tags)``.
  .. [LMP01] Lafferty, J., McCallum, A., Pereira, F. (2001).
     "Conditional random fields: Probabilistic models for segmenting and
     labeling sequence data". *Proc. 18th International Conf. on Machine
     Learning*. Morgan Kaufmann. pp. 282–289.
  .. _Viterbi algorithm: https://en.wikipedia.org/wiki/Viterbi_algorithm
code: def _compute_score(
      self,
      emissions: torch.Tensor,
      tags: torch.LongTensor,
      mask: torch.ByteTensor,
  ) -> torch.Tensor:
      # emissions: (seq_length, batch_size, num_tags)
      # tags: (seq_length, batch_size)
      # mask: (seq_length, batch_size)
      assert emissions.dim() == 3 and tags.dim() == 2
      assert emissions.shape[:2] == tags.shape
      assert emissions.size(2) == self.num_tags
      assert mask.shape == tags.shape
      assert mask[0].all()
  
      seq_length, batch_size = tags.shape
      mask = mask.float()
  
      # Start transition score and first emission
      # shape: (batch_size,)
      score = self.start_transitions[tags[0]]
      score += emissions[0, torch.arange(batch_size), tags[0]]
  
      for i in range(1, seq_length):
          # Transition score to next tag, only added if next timestep is valid (mask == 1)
          # shape: (batch_size,)
          score += self.transitions[tags[i - 1], tags[i]] * mask[i]
  
          # Emission score for next tag, only added if next timestep is valid (mask == 1)
          # shape: (batch_size,)
          score += (
              emissions[i, torch.arange(batch_size), tags[i]] * mask[i]
          )
  
      # End transition score
      # shape: (batch_size,)
      seq_ends = mask.long().sum(dim=0) - 1
      # shape: (batch_size,)
      last_tags = tags[seq_ends, torch.arange(batch_size)]
      # shape: (batch_size,)
      score += self.end_transitions[last_tags]
  
      return score
  
file_path: nlp-architect/nlp_architect/nn/torch/layers/crf.py
imports: from typing import List, Optional
  import torch
  import torch.nn
label_desc: Use image node_id 7 for calling the CRF obj's underlying member method code with example usage: obj._compute_score(emissions, tags, mask) and returns: score