class_docstr: Return the PPS (Predictive Power Score) of all properties in relation to the label.
  
  The PPS represents the ability of a feature to single-handedly predict another feature or label.
  In this check, we specifically use it to assess the ability to predict the label by a text property (e.g.
  text length, language etc.).
  A high PPS (close to 1) can mean that there's a bias in the dataset, as a single property can predict the label
  successfully, using simple classic ML algorithms - meaning that a deep learning algorithm may accidentally learn
  these properties instead of more accurate complex abstractions.
  
  Uses the ppscore package - for more info, see https://github.com/8080labs/ppscore
  
  Parameters
  ----------
  properties_to_ignore: Optional[List[str]], default: None
      List of properties to ignore in the check.
  properties_to_include: Optional[List[str]], default: None
      List of properties to include in the check. If None, all properties will be included.
  ppscore_params : dict , default: None
      dictionary of additional parameters for the ppscore.predictors function
  n_top_properties : int , default: 5
      Number of properties to show, sorted by the magnitude of difference in PPS
  n_samples : int , default: 100_000
      number of samples to use for this check.
  random_state : int , default: None
      Random state for the ppscore.predictors function
docstr: Add condition that will check that pps of the specified properties is less than the threshold.
  
  Parameters
  ----------
  threshold : float , default: 0.3
      pps upper bound
  Returns
  -------
  FLC
docstr_tok: ["Add","condition","that","will","check","that","pps","of","the","specified","properties","is","less","than","the","threshold",".","Parameters","--","--","--","--","--","threshold",":","float",",","default",":","0.3","pps","upper","bound","Returns","--","--","--","-","FLC"]
code: def add_condition_property_pps_less_than(
      self: PLC, threshold: float = 0.3
  ) -> PLC:
      """
      Add condition that will check that pps of the specified properties is less than the threshold.
  
      Parameters
      ----------
      threshold : float , default: 0.3
          pps upper bound
      Returns
      -------
      FLC
      """
  
      def condition(value: t.Dict[Hashable, float]) -> ConditionResult:
          failed_properties = {
              property_name: format_number(pps_value)
              for property_name, pps_value in value.items()
              if pps_value >= threshold
          }
  
          if failed_properties:
              message = (
                  f"Found {len(failed_properties)} out of {len(value)} properties with PPS above threshold: "
                  f"{failed_properties}"
              )
              return ConditionResult(ConditionCategory.FAIL, message)
          else:
              return ConditionResult(
                  ConditionCategory.PASS,
                  get_condition_passed_message(value),
              )
  
      return self.add_condition(
          f"Properties' Predictive Power Score is less than {format_number(threshold)}",
          condition,
      )
  
file_path: deepchecks/deepchecks/nlp/checks/data_integrity/property_label_correlation.py
imports: import typing
  import pandas
  import deepchecks.ppscore
  from deepchecks.core import CheckResult, ConditionCategory, ConditionResult
  from deepchecks.core.check_utils.feature_label_correlation_utils import get_pps_figure, pd_series_to_trace
  from deepchecks.core.errors import DatasetValidationError
  from deepchecks.nlp import Context, SingleDatasetCheck
  from deepchecks.nlp.task_type import TaskType
  from deepchecks.tabular.utils.messages import get_condition_passed_message
  from deepchecks.utils.strings import format_number
  from deepchecks.utils.typing import Hashable
label_desc: Use image node_id 3 for calling the PropertyLabelCorrelation obj's underlying member method code with example usage: obj.add_condition_property_pps_less_than(threshold) and returns: self, ConditionResult, ConditionResult