code: def after_epoch(self, epoch_id: int, epoch_data: EpochData):
          
  
          if self._stream not in epoch_data:
              raise KeyError('The hook could not determine whether the threshold was exceeded as the stream `{}`'
                             'was not found in the epoch data'.format(self._stream))
  
          if self._variable not in epoch_data[self._stream]:
              raise KeyError('The hook could not determine whether the threshold was exceeded as the variable `{}`'
                             'was not found in the epoch data stream `{}`'.format(self._variable, self._stream))
  
          value = epoch_data[self._stream][self._variable]
          if isinstance(value, dict) and 'mean' in value:
              value = value['mean']
  
          if not np.isscalar(value):
              raise TypeError('The value to be checked has to be either a scalar or a dict with `mean` key. '
                              'Got `{}` instead.'.format(type(value).__name__))
  
          if value > self._required_min_value:
              raise TrainingTerminated('{} {} level matched (current {} is greater than required {}).'
                                       .format(self._stream, self._variable, value, self._required_min_value))
          elif epoch_id >= self._max_epoch:
              raise ValueError('{} {} was only {} in epoch {}, but {} was required. Training failed.'
                               .format(self._stream, self._variable, value, epoch_id, self._required_min_value))
docs: Check termination conditions.
  
          :param epoch_id: number of the processed epoch
          :param epoch_data: epoch data to be checked
          :raise KeyError: if the stream of variable was not found in ``epoch_data``
          :raise TypeError: if the monitored variable is not a scalar or scalar ``mean`` aggregation
          :raise ValueError: if the specified number of epochs exceeded
          :raise TrainingTerminated: if the monitor variable is above the required level