class_docstr: Summarize mean average precision metrics on a dataset and model per IoU and bounding box area.
  
  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","precision","metrics","on","a","dataset","and","model","per","IoU","and","bounding","box","area",".","Parameters","--","--","--","--","--","area_range",":","tuple",",","default",":","(","32","*","*","2",",","96","*","*","2",")","Slices","for","small\/medium\/large","buckets",".","{","additional_check_init_params:2","*","indent","}"]
code: def __init__(
      self,
      area_range: Tuple = (32**2, 96**2),
      n_samples: Optional[int] = 10000,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.n_samples = n_samples
      self.area_range = area_range
  
imports: import math
  from typing import Optional, Tuple, TypeVar
  import numpy
  import pandas
  import plotly.express
  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 1 to create a new MeanAveragePrecisionReport object from inherited base classes: SingleDatasetCheck with example: obj = MeanAveragePrecisionReport(area_range, n_samples)