code: def __call__(
      self,
      object_predictions: List[ObjectPrediction],
  ):
      try:
          from lsnms import nms
      except ModuleNotFoundError:
          raise ModuleNotFoundError(
              'Please run "pip install lsnms>0.3.1" to install lsnms first for lsnms utilities.'
          )
  
      if self.match_metric == "IOS":
          NotImplementedError(
              f"match_metric={self.match_metric} is not supported for LSNMSPostprocess"
          )
  
      logger.warning(
          "LSNMSPostprocess is experimental and not recommended to use."
      )
  
      object_prediction_list = ObjectPredictionList(object_predictions)
      object_predictions_as_numpy = object_prediction_list.tonumpy()
  
      boxes = object_predictions_as_numpy[:, :4]
      scores = object_predictions_as_numpy[:, 4]
      class_ids = object_predictions_as_numpy[:, 5].astype("uint8")
  
      keep = nms(
          boxes,
          scores,
          iou_threshold=self.match_threshold,
          class_ids=None if self.class_agnostic else class_ids,
      )
  
      selected_object_predictions = object_prediction_list[
          keep
      ].tolist()
      if not isinstance(selected_object_predictions, list):
          selected_object_predictions = [selected_object_predictions]
  
      return selected_object_predictions
  
imports: import logging
  from typing import List
  import torch
  from sahi.postprocess.utils import ObjectPredictionList, has_match, merge_object_prediction_pair
  from sahi.prediction import ObjectPrediction
  from sahi.utils.import_utils import check_requirements
label_desc: Use image node_id 1 for calling the LSNMSPostprocess obj's underlying member method code with example usage: obj.__call__(object_predictions) and returns: selected_object_predictions