parameters: (
          boxes: NodeInput,
          scores: NodeInput,
          sort_result_type: str = "none",
          sort_result_across_batch: bool = False,
          output_type: str = "i64",
          iou_threshold: float = 0.0,
          score_threshold: float = 0.0,
          nms_top_k: int = -1,
          keep_top_k: int = -1,
          background_class: int = -1,
          nms_eta: float = 1.0,
          normalized: bool = True
  )
return_statement: return _get_node_factory_opset8().create("MulticlassNms", inputs, attributes)
docstring: Return a node which performs MulticlassNms.
  
      @param boxes: Tensor with box coordinates.
      @param scores: Tensor with box scores.
      @param sort_result_type: Specifies order of output elements, possible values:
                               'class': sort selected boxes by class id (ascending)
                               'score': sort selected boxes by score (descending)
                               'none': do not guarantee the order.
      @param sort_result_across_batch: Specifies whenever it is necessary to sort selected boxes
                                       across batches or not
      @param output_type: Specifies the output tensor type, possible values:
                          'i64', 'i32'
      @param iou_threshold: Specifies intersection over union threshold
      @param score_threshold: Specifies minimum score to consider box for the processing
      @param nms_top_k: Specifies maximum number of boxes to be selected per class, -1 meaning
                        to keep all boxes
      @param keep_top_k: Specifies maximum number of boxes to be selected per batch element, -1
                         meaning to keep all boxes
      @param background_class: Specifies the background class id, -1 meaning to keep all classes
      @param nms_eta: Specifies eta parameter for adpative NMS, in close range [0, 1.0]
      @param normalized: Specifies whether boxes are normalized or not
      @return: The new node which performs MuticlassNms
docstring_summary: Return a node which performs MulticlassNms.
docstring_tokens: Return a node which performs MulticlassNms .
function: def multiclass_nms(
          boxes: NodeInput,
          scores: NodeInput,
          sort_result_type: str = "none",
          sort_result_across_batch: bool = False,
          output_type: str = "i64",
          iou_threshold: float = 0.0,
          score_threshold: float = 0.0,
          nms_top_k: int = -1,
          keep_top_k: int = -1,
          background_class: int = -1,
          nms_eta: float = 1.0,
          normalized: bool = True
  ) -> Node:
      """Return a node which performs MulticlassNms.
  
      @param boxes: Tensor with box coordinates.
      @param scores: Tensor with box scores.
      @param sort_result_type: Specifies order of output elements, possible values:
                               'class': sort selected boxes by class id (ascending)
                               'score': sort selected boxes by score (descending)
                               'none': do not guarantee the order.
      @param sort_result_across_batch: Specifies whenever it is necessary to sort selected boxes
                                       across batches or not
      @param output_type: Specifies the output tensor type, possible values:
                          'i64', 'i32'
      @param iou_threshold: Specifies intersection over union threshold
      @param score_threshold: Specifies minimum score to consider box for the processing
      @param nms_top_k: Specifies maximum number of boxes to be selected per class, -1 meaning
                        to keep all boxes
      @param keep_top_k: Specifies maximum number of boxes to be selected per batch element, -1
                         meaning to keep all boxes
      @param background_class: Specifies the background class id, -1 meaning to keep all classes
      @param nms_eta: Specifies eta parameter for adpative NMS, in close range [0, 1.0]
      @param normalized: Specifies whether boxes are normalized or not
      @return: The new node which performs MuticlassNms
      """
      inputs = as_nodes(boxes, scores)
  
      attributes = {
          "sort_result_type": sort_result_type,
          "sort_result_across_batch": sort_result_across_batch,
          "output_type": output_type,
          "iou_threshold": iou_threshold,
          "score_threshold": score_threshold,
          "nms_top_k": nms_top_k,
          "keep_top_k": keep_top_k,
          "background_class": background_class,
          "nms_eta": nms_eta,
          "normalized": normalized
      }
  
      return _get_node_factory_opset8().create("MulticlassNms", inputs, attributes)
function_tokens: def multiclass_nms ( boxes : NodeInput , scores : NodeInput , sort_result_type : str = "none" , sort_result_across_batch : bool = False , output_type : str = "i64" , iou_threshold : float = 0.0 , score_threshold : float = 0.0 , nms_top_k : int = - 1 , keep_top_k : int = - 1 , background_class : int = - 1 , nms_eta : float = 1.0 , normalized : bool = True ) -> Node : inputs = as_nodes ( boxes , scores ) attributes = { "sort_result_type" : sort_result_type , "sort_result_across_batch" : sort_result_across_batch , "output_type" : output_type , "iou_threshold" : iou_threshold , "score_threshold" : score_threshold , "nms_top_k" : nms_top_k , "keep_top_k" : keep_top_k , "background_class" : background_class , "nms_eta" : nms_eta , "normalized" : normalized } return _get_node_factory_opset8 ( ) . create ( "MulticlassNms" , inputs , attributes )