class_docstr: Find outliers labels with respect to the given properties.
  
  The check computes several label properties and then computes the number of outliers for each property.
  The check uses `IQR <https://en.wikipedia.org/wiki/Interquartile_range#Outliers>`_ to detect outliers out of the
  single dimension properties.
  
  Parameters
  ----------
  label_properties : List[Dict[str, Any]], default: None
      List of properties. Replaces the default deepchecks properties.
      Each property is a dictionary with keys ``'name'`` (str), ``method`` (Callable) and ``'output_type'`` (str),
      representing attributes of said method. 'output_type' must be one of:
  
      - ``'numerical'`` - for continuous ordinal outputs.
      - ``'categorical'`` - for discrete, non-ordinal outputs. These can still be numbers,
        but these numbers do not have inherent value.
      - ``'class_id'`` - for properties that return the class_id. This is used because these
        properties are later matched with the ``VisionData.label_map``, if one was given.
  
      For more on image / label properties, see the guide about :ref:`vision__properties_guide`.
  n_show_top : int , default: 3
      number of outliers to show from each direction (upper limit and bottom limit)
  iqr_percentiles: Tuple[int, int], default: (25, 75)
      Two percentiles which define the IQR range
  iqr_scale: float, default: 1.5
      The scale to multiply the IQR range for the outliers detection
  min_samples : int , default: 10
      Minimum number of samples required to calculate IQR. If there are not enough non-null samples a specific
      property, the check will skip it. If all properties are skipped, the check will raise a NotEnoughSamplesError.
  {additional_check_init_params:2*indent}
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code: def __init__(
      self,
      label_properties: t.List[t.Dict[str, t.Any]] = None,
      n_show_top: int = 3,
      iqr_percentiles: t.Tuple[int, int] = (25, 75),
      iqr_scale: float = 1.5,
      min_samples: int = 10,
      n_samples: t.Optional[int] = 10000,
      **kwargs,
  ):
      super().__init__(
          properties_list=label_properties,
          property_input_type=PropertiesInputType.LABELS,
          n_show_top=n_show_top,
          iqr_percentiles=iqr_percentiles,
          iqr_scale=iqr_scale,
          draw_label_on_image=True,
          min_samples=min_samples,
          n_samples=n_samples,
          **kwargs,
      )
  
file_path: deepchecks/deepchecks/vision/checks/data_integrity/label_property_outliers.py
imports: import typing
  from deepchecks.core.errors import DeepchecksProcessError
  from deepchecks.vision._shared_docs import docstrings
  from deepchecks.vision.checks.data_integrity.abstract_property_outliers import AbstractPropertyOutliers
  from deepchecks.vision.utils import label_prediction_properties
  from deepchecks.vision.utils.vision_properties import PropertiesInputType
  from deepchecks.vision.vision_data import TaskType, VisionData
label_desc: Use image node_id 1 to create a new LabelPropertyOutliers object from inherited base classes: AbstractPropertyOutliers with example: obj = LabelPropertyOutliers(label_properties, n_show_top, iqr_percentiles, iqr_scale, min_samples, n_samples)