                   "or higher will see the greatest performance benefit from mixed precision "
                   "because they have Tensor Cores. Older GPUs offer no math performance benefit "
                   "for using mixed precision, however memory and bandwidth savings can enable some "
                   "speedups. Generally RTX GPUs and later will offer the most benefit.")
  
      def load_module(self, filename, module_path, plugin_type):
          """ Load the defaults module and add defaults """
          logger.debug("Adding defaults: (filename: %s, module_path: %s, plugin_type: %s",
                       filename, module_path, plugin_type)
 
</s> remove     @staticmethod
    def _get_batchsize(queue_size):
        """ Get the batch size for feeding the model.

        Sets the batch size to 1 if inference is being run on CPU, otherwise the minimum of the
        :attr:`self._queue_size` and 16.

        Returns
        -------
        int
            The batch size that the model is to be fed at.
        """
        logger.debug("Getting batchsize")
        is_cpu = GPUStats().device_count == 0
        batchsize = 1 if is_cpu else 16
        batchsize = min(queue_size, batchsize)
        logger.debug("Batchsize: %s", batchsize)
        logger.debug("Got batchsize: %s", batchsize)
        return batchsize

 </s> add  </s> remove         self._batchsize = self._get_batchsize(queue_size)
 </s> add 
        self.add_item(
            section=section,
            title="convert_batchsize",
            datatype=int,
            default=16,
            min_max=(1, 32),
            rounding=1,
            group="convert",
            info="[GPU Only]. The number of faces to feed through the model at once when running "
                 "the Convert process.\n\nNB: Increasing this figure is unlikely to improve "
                 "convert speed, however, if you are getting Out of Memory errors, then you may "
                 "want to reduce the batch size.")
                   "or higher will see the greatest performance benefit from mixed precision "
                   "because they have Tensor Cores. Older GPUs offer no math performance benefit "
                   "for using mixed precision, however memory and bandwidth savings can enable some "
                   "speedups. Generally RTX GPUs and later will offer the most benefit.")
        self.add_item(
            section=section,
            title="convert_batchsize",
            datatype=int,
            default=16,
            min_max=(1, 32),
            rounding=1,
            group="convert",
            info="[GPU Only]. The number of faces to feed through the model at once when running "
                 "the Convert process.\n\nNB: Increasing this figure is unlikely to improve "
                 "convert speed, however, if you are getting Out of Memory errors, then you may "
                 "want to reduce the batch size.")
  
      def load_module(self, filename, module_path, plugin_type):
          """ Load the defaults module and add defaults """
          logger.debug("Adding defaults: (filename: %s, module_path: %s, plugin_type: %s",
                       filename, module_path, plugin_type)
keep keep keep add keep keep keep keep keep
 model.config - Make convert batchsize a user configurable option 
plugins/train/_config.py