class_docstr: Defines an evaluator for the LVIS dataset.
  
  This evaluator is usually used through a metric returned by
  :func:`make_lvis_metrics`.
  
  This mostly acts a wrapper around :class:`LVISEval` from the `lvis` library.
code: def update(self, predictions: TCommonDetectionOutput):
      img_ids = list(np.unique(list(predictions.keys())))
      self.img_ids.extend(img_ids)
  
      results = self.prepare_for_lvis_detection(predictions)
      self.predictions.extend(results)
  
imports: import copy
  import itertools
  import logging
  from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  import pycocotools.mask
  import torch
  import torch.distributed
  from lvis import LVISEval, LVISResults, LVIS
  from avalanche.evaluation.metrics.detection import DetectionEvaluator, TCommonDetectionOutput
label_desc: Use image node_id 2 for calling the LvisEvaluator obj's underlying member method code with example usage: obj.update(predictions) without return types