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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docstr: Return default properties to run in the check.
docstr_tok: ["Return","default","properties","to","run","in","the","check","."]
code: def get_default_properties(self, data: VisionData):
      """Return default properties to run in the check."""
      if data.task_type == TaskType.CLASSIFICATION:
          raise DeepchecksProcessError(
              "task type classification does not have default label "
              "properties for label outliers."
          )
      elif data.task_type == TaskType.OBJECT_DETECTION:
          return (
              label_prediction_properties.DEFAULT_OBJECT_DETECTION_LABEL_PROPERTIES
          )
      elif data.task_type == TaskType.SEMANTIC_SEGMENTATION:
          return (
              label_prediction_properties.DEFAULT_SEMANTIC_SEGMENTATION_LABEL_PROPERTIES
          )
      else:
          raise DeepchecksProcessError(
              f"task type {data.task_type} does not have default label "
              f"properties defined."
          )
  
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 2 for calling the LabelPropertyOutliers obj's underlying member method code with example usage: obj.get_default_properties(data) and returns: label_prediction_properties, label_prediction_properties