class_docstr: Summarize mean average recall metrics on a dataset and model per detections and area range.
  
  Parameters
  ----------
  area_range: tuple, default: (32**2, 96**2)
      Slices for small/medium/large buckets.
  {additional_check_init_params:2*indent}
class_docstr_tok: ["Summarize","mean","average","recall","metrics","on","a","dataset","and","model","per","detections","and","area","range",".","Parameters","--","--","--","--","--","area_range",":","tuple",",","default",":","(","32","*","*","2",",","96","*","*","2",")","Slices","for","small\/medium\/large","buckets",".","{","additional_check_init_params:2","*","indent","}"]
docstr: Update the metrics by passing the batch to ignite metric update method.
docstr_tok: ["Update","the","metrics","by","passing","the","batch","to","ignite","metric","update","method","."]
code: def update(
      self,
      context: Context,
      batch: BatchWrapper,
      dataset_kind: DatasetKind,
  ):
      """Update the metrics by passing the batch to ignite metric update method."""
      self._ap_metric.update(
          (batch.numpy_predictions, batch.numpy_labels)
      )
  
file_path: deepchecks/deepchecks/vision/checks/model_evaluation/mean_average_recall_report.py
imports: import math
  from typing import Optional, Tuple, TypeVar
  import numpy
  import pandas
  from deepchecks.core import CheckResult, ConditionResult, DatasetKind
  from deepchecks.core.condition import ConditionCategory
  from deepchecks.utils.strings import format_number
  from deepchecks.vision._shared_docs import docstrings
  from deepchecks.vision.base_checks import SingleDatasetCheck
  from deepchecks.vision.context import Context
  from deepchecks.vision.metrics_utils.detection_precision_recall import ObjectDetectionAveragePrecision
  from deepchecks.vision.vision_data import TaskType
  from deepchecks.vision.vision_data.batch_wrapper import BatchWrapper
label_desc: Use image node_id 3 for calling the MeanAverageRecallReport obj's underlying member method code with example usage: obj.update(context, batch, dataset_kind) without return types