code: def warn_parameterisation(
      metric_name: str = "Metric",
      sensitive_params: str = "X, Y and Z.",
      data_domain_applicability: str = "",
      citation: str = "INSERT CITATION",
  ):
      """
      Warn the parameterisation of the metric.
  
      Parameters
      ----------
      metric_name: string
          The metric name.
      sensitive_params: string
          The sensitive parameters of the metric.
      data_domain_applicability string
          The applicability when it comes to data domains, default = "".
      citation: string
          The citation.
  
      Returns
      -------
      None
      """
  
      time.sleep(1)
  
      print("Warnings and information:")
      text = (
          f" (1) The {get_name(metric_name)} metric is likely to be sensitive to the choice of "
          f"{sensitive_params}. {data_domain_applicability} \n (2) If attributions are normalised or their absolute values are taken it may "
          f"destroy or skew information in the explanation and as a result, affect the overall evaluation outcome."
          f"\n (3) Make sure to validate the choices for hyperparameters of the metric (by calling"
          f" .get_params of the metric instance).\n (4) For further information, see original publication: {citation}."
          f"\n (5) To disable these warnings set 'disable_warnings' = True when initialising the metric.\n"
      )
      print(text)
  
file_path: Quantus/quantus/helpers/warn.py
imports: import time
  import warnings
  import numpy
  from quantus.helpers.utils import get_name
label_desc: Use image node_id 9 for calling a global function with example usage: warn_parameterisation(metric_name, sensitive_params, data_domain_applicability, citation) without return types