class_docstr: Compute the log-likelihood of the input assuming a conditional random field
  model.
  
  Args:
      num_tags: The number of tags
class_docstr_tok: ["Compute","the","log-likelihood","of","the","input","assuming","a","conditional","random","field","model",".","Args",":","num_tags",":","The","number","of","tags"]
docstr: Given a set of emission probabilities, return the predicted tags.
  
  Args:
      emissions: Emission probabilities with expected shape of
          batch_size * seq_len * num_labels
      seq_lens: Length of each input.
docstr_tok: ["Given","a","set","of","emission","probabilities",",","return","the","predicted","tags",".","Args",":","emissions",":","Emission","probabilities","with","expected","shape","of","batch_size","*","seq_len","*","num_labels","seq_lens",":","Length","of","each","input","."]
code: def decode(
      self, emissions: torch.Tensor, seq_lens: torch.Tensor
  ) -> torch.Tensor:
      """
      Given a set of emission probabilities, return the predicted tags.
  
      Args:
          emissions: Emission probabilities with expected shape of
              batch_size * seq_len * num_labels
          seq_lens: Length of each input.
      """
      mask = self._make_mask_from_seq_lens(seq_lens)
      result = self._viterbi_decode(emissions, mask)
      return result
  
imports: from typing import List
  import torch
  import torch.jit
  import torch.nn
  from caffe2.python.crf_predict import apply_crf
  from pytext.utils.usage import log_class_usage
label_desc: Use image node_id 6 for calling the CRF obj's underlying member method code with example usage: obj.decode(emissions, seq_lens) and returns: result