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
docstr: Initialize the transition parameters.
  The parameters will be initialized randomly from a uniform distribution
  between -0.1 and 0.1.
docstr_tok: ["Initialize","the","transition","parameters",".","The","parameters","will","be","initialized","randomly","from","a","uniform","distribution","between","-0.1","and","0.1","."]
code: def reset_parameters(self) -> None:
      """Initialize the transition parameters.
      The parameters will be initialized randomly from a uniform distribution
      between -0.1 and 0.1.
      """
      nn.init.uniform_(self.start_transitions, -0.1, 0.1)
      nn.init.uniform_(self.end_transitions, -0.1, 0.1)
      nn.init.uniform_(self.transitions, -0.1, 0.1)
  
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 2 for calling the CRF obj's underlying member method code with example usage: obj.reset_parameters() without return types