class_docstr: Find outliers with respect to the given properties.
  
  The check finds outliers in the text properties.
  For numeric properties, the check uses `IQR <https://en.wikipedia.org/wiki/Interquartile_range#Outliers>`_ to
  detect outliers out of the single dimension properties.
  For categorical properties, the check searches for a relative "sharp drop" in values in order to detect outliers.
  
  Parameters
  ----------
  n_show_top : int , default : 5
      number of graphs to show (ordered from the property with the most outliers to the least)
  iqr_percentiles : Tuple[int, int] , default : (25, 75)
      Two percentiles which define the IQR range
  iqr_scale : float , default : 2
      The scale to multiply the IQR range for the outliers' detection
  sharp_drop_ratio : float, default : 0.9
      The size of the sharp drop to detect categorical outliers
  min_samples : int , default : 10
      Minimum number of samples required to calculate IQR. If there are not enough non-null samples for a specific
      property, the check will skip it. If all properties are skipped, the check will raise a NotEnoughSamplesError.
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code: def __init__(
      self,
      n_show_top: int = 5,
      iqr_percentiles: t.Tuple[int, int] = (25, 75),
      iqr_scale: float = 2,
      sharp_drop_ratio: float = 0.9,
      min_samples: int = 10,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.iqr_percentiles = iqr_percentiles
      self.iqr_scale = iqr_scale
      self.sharp_drop_ratio = sharp_drop_ratio
      self.n_show_top = n_show_top
      self.min_samples = min_samples
  
file_path: deepchecks/deepchecks/nlp/checks/data_integrity/text_property_outliers.py
imports: import typing
  import numpy
  import pandas
  from typing_extensions import Self
  from deepchecks import ConditionCategory, ConditionResult
  from deepchecks.core import CheckResult, DatasetKind
  from deepchecks.core.errors import DeepchecksValueError, NotEnoughSamplesError
  from deepchecks.nlp import Context, SingleDatasetCheck
  from deepchecks.nlp.utils.nlp_plot import get_text_outliers_graph
  from deepchecks.utils.dataframes import hide_index_for_display
  from deepchecks.utils.outliers import iqr_outliers_range, sharp_drop_outliers_range
  from deepchecks.utils.strings import format_percent
label_desc: Use image node_id 1 to create a new TextPropertyOutliers object from inherited base classes: SingleDatasetCheck with example: obj = TextPropertyOutliers(n_show_top, iqr_percentiles, iqr_scale, sharp_drop_ratio, min_samples)