content: from ..registry import RECOGNIZERS
  from .base import BaseRecognizer
  
  
  @RECOGNIZERS.register_module()
  class AudioRecognizer(BaseRecognizer):
      """Audio recognizer model framework."""
  
      def forward(self, audios, label=None, return_loss=True):
          """Define the computation performed at every call."""
          if return_loss:
              if label is None:
                  raise ValueError('Label should not be None.')
              return self.forward_train(audios, label)
  
          return self.forward_test(audios)
  
      def forward_train(self, audios, labels):
          """Defines the computation performed at every call when training."""
          audios = audios.reshape((-1, ) + audios.shape[2:])
          x = self.extract_feat(audios)
          cls_score = self.cls_head(x)
          gt_labels = labels.squeeze()
          loss = self.cls_head.loss(cls_score, gt_labels)
  
          return loss
  
      def forward_test(self, audios):
          """Defines the computation performed at every call when evaluation and
          testing."""
          num_segs = audios.shape[1]
          audios = audios.reshape((-1, ) + audios.shape[2:])
          x = self.extract_feat(audios)
          cls_score = self.cls_head(x)
          cls_score = self.average_clip(cls_score, num_segs)
  
          return cls_score.cpu().numpy()
  
      def forward_gradcam(self, audios):
          raise NotImplementedError
  
      def train_step(self, data_batch, optimizer, **kwargs):
          """The iteration step during training.
  
          This method defines an iteration step during training, except for the
          back propagation and optimizer updating, which are done in an optimizer
          hook. Note that in some complicated cases or models, the whole process
          including back propagation and optimizer updating is also defined in
          this method, such as GAN.
  
          Args:
              data_batch (dict): The output of dataloader.
              optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of
                  runner is passed to ``train_step()``. This argument is unused
                  and reserved.
  
          Returns:
              dict: It should contain at least 3 keys: ``loss``, ``log_vars``,
                  ``num_samples``.
                  ``loss`` is a tensor for back propagation, which can be a
                  weighted sum of multiple losses.
                  ``log_vars`` contains all the variables to be sent to the
                  logger.
                  ``num_samples`` indicates the batch size (when the model is
                  DDP, it means the batch size on each GPU), which is used for
                  averaging the logs.
          """
          audios = data_batch['audios']
          label = data_batch['label']
  
          losses = self(audios, label)
  
          loss, log_vars = self._parse_losses(losses)
  
          outputs = dict(
              loss=loss,
              log_vars=log_vars,
              num_samples=len(next(iter(data_batch.values()))))
  
          return outputs
  
      def val_step(self, data_batch, optimizer, **kwargs):
          """The iteration step during validation.
  
          This method shares the same signature as :func:`train_step`, but used
          during val epochs. Note that the evaluation after training epochs is
          not implemented with this method, but an evaluation hook.
          """
          audios = data_batch['audios']
          label = data_batch['label']
  
          losses = self(audios, label)
  
          loss, log_vars = self._parse_losses(losses)
  
          outputs = dict(
              loss=loss,
              log_vars=log_vars,
              num_samples=len(next(iter(data_batch.values()))))
  
          return outputs
  