class_docstr: RCNN L1 loss. 
docstr: Updates the internal evaluation result. 
docstr_tok: ["Updates","the","internal","evaluation","result","."]
code: def update(self, labels, preds):
      """Updates the internal evaluation result."""
      # label = [rcnn_bbox_target, rcnn_bbox_weight]
      # pred = [rcnn_reg]
      rcnn_bbox_target, rcnn_bbox_weight = labels
      rcnn_bbox_reg = preds[0]
  
      # calculate num_inst
      num_inst = mx.nd.sum(rcnn_bbox_weight) / 4
  
      # calculate smooth_l1
      loss = mx.nd.sum(
          rcnn_bbox_weight
          * mx.nd.smooth_l1(rcnn_bbox_reg - rcnn_bbox_target, scalar=1)
      )
  
      self.sum_metric += loss.asscalar()
      self.num_inst += num_inst.asscalar()
  
imports: import mxnet
label_desc: Use image node_id 2 for calling the RCNNL1LossMetric obj's underlying member method code with example usage: obj.update(labels, preds) without return types