code: def forward(self,
                  inputs: torch.Tensor,
                  tags: torch.Tensor,
                  mask: torch.ByteTensor = None) -> torch.Tensor:
          
          # pylint: disable=arguments-differ
          if mask is None:
              mask = torch.ones(*tags.size(), dtype=torch.long)
  
          log_denominator = self._input_likelihood(inputs, mask)
          log_numerator = self._joint_likelihood(inputs, tags, mask)
  
          return torch.sum(log_numerator - log_denominator)
docs: Computes the log likelihood.