code: def select_threshold(
      model=None,
      data=None,
      curve=None,
      FPR=None,
      FNR=None,
      thread_count=-1,
  ):
      """
      Selects a threshold for prediction.
  
      Parameters
      ----------
      model : catboost.CatBoost
          The trained model.
  
      data : catboost.Pool or list of catboost.Pool
          Set of samples to build ROC curve with.
          If set, curve parameter must not be set.
  
      curve : tuple of three arrays (fpr, tpr, thresholds)
          ROC curve points in format of get_roc_curve returned value.
          If set, data parameter must not be set.
  
      FPR : desired false-positive rate
  
      FNR : desired false-negative rate (only one of FPR and FNR should be chosen)
  
      thread_count : int (default=-1)
          Number of threads to work with.
          If -1, then the number of threads is set to the number of CPU cores.
  
      Returns
      -------
      threshold : double
      """
      if data is not None:
          if curve is not None:
              raise CatBoostError(
                  "Only one of the parameters data and curve should be set."
              )
          if model is None:
              raise CatBoostError(
                  "model and data parameters should be set when curve parameter is None."
              )
          if type(data) == Pool:
              data = [data]
          if not isinstance(data, list):
              raise CatBoostError(
                  "data must be a catboost.Pool or list of pools."
              )
          for pool in data:
              if not isinstance(pool, Pool):
                  raise CatBoostError(
                      "one of data pools is not catboost.Pool"
                  )
          return _select_threshold(
              model._object, data, None, FPR, FNR, thread_count
          )
      elif curve is not None:
          if (
              not (isinstance(curve, list) or isinstance(curve, tuple))
              or len(curve) != 3
          ):
              raise CatBoostError(
                  "curve must be list or tuple of three arrays (fpr, tpr, thresholds)."
              )
          return _select_threshold(
              None, None, curve, FPR, FNR, thread_count
          )
      else:
          raise CatBoostError(
              "One of the parameters data and curve should be set."
          )
  
imports: from .None import _catboost
  from .core import Pool, CatBoostError, ARRAY_TYPES, PATH_TYPES, fspath, _update_params_quantize_part, _process_synonyms
  from collections import defaultdict
  from contextlib import contextmanager
  import sys
  import numpy
  import warnings
label_desc: Use image node_id 12 for calling a global function with example usage: select_threshold(model, data, curve, FPR, FNR, thread_count) and returns: _select_threshold, _select_threshold