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 __init__(self, lvis_gt: LVIS, iou_types: List[str]):
      assert isinstance(iou_types, (list, tuple))
      self.lvis_gt = lvis_gt
  
      self.iou_types = iou_types
      self.img_ids: List[int] = []
      self.predictions: List[Dict[str, Any]] = []
      self.lvis_eval_per_iou: Dict[str, LVISEval] = dict()
  
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 1 to create a new LvisEvaluator object from inherited base classes: DetectionEvaluator with example: obj = LvisEvaluator(lvis_gt, iou_types)