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
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code: def __repr__(self) -> str:
      return f"{self.__class__.__name__}(num_tags={self.num_tags})"
  
file_path: lightseq/tests/torch_crf.py
imports: import torch
  import torch.nn
  from typing import List, Optional
label_desc: Use image node_id 3 for calling the CRF obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, self, str