code: def sensitivity_specificity_cutoff(tpr, fpr):
      """Find index of optimal cutoff point on curve.
  
      Cut-off is determined using Youden's index defined as sensitivity + specificity - 1.
  
      Parameters
      ----------
      tpr : array, shape = [n_roc_points]
          True positive rate per threshold
      fpr : array, shape = [n_roc_points]
          False positive rate per threshold
  
      References
      ----------
      Ewald, B. (2006). Post hoc choice of cut points introduced bias to diagnostic research.
      Journal of clinical epidemiology, 59(8), 798-801.
  
      Steyerberg, E.W., Van Calster, B., & Pencina, M.J. (2011). Performance measures for
      prediction models and markers: evaluation of predictions and classifications.
      Revista Espanola de Cardiologia (English Edition), 64(9), 788-794.
  
      Jiménez-Valverde, A., & Lobo, J.M. (2007). Threshold criteria for conversion of probability
      of species presence to either–or presence–absence. Acta oecologica, 31(3), 361-369.
      """
      return np.argmax(tpr - fpr)
  
file_path: deepchecks/deepchecks/tabular/checks/model_evaluation/roc_report.py
imports: from typing import Dict, List
  import numpy
  import plotly.graph_objects
  import sklearn
  from deepchecks.core import CheckResult, ConditionResult
  from deepchecks.core.condition import ConditionCategory
  from deepchecks.core.errors import DeepchecksNotSupportedError
  from deepchecks.tabular import Context, SingleDatasetCheck
  from deepchecks.tabular.utils.task_type import TaskType
  from deepchecks.utils.dict_funcs import get_dict_entry_by_value
  from deepchecks.utils.strings import format_number
label_desc: Use image node_id 2 for calling a global function with example usage: sensitivity_specificity_cutoff(tpr, fpr) and returns: np