code: def _outlierDetection_threaded(inputs):
      """
      Detect the outlier
      """
      [samples_idx, samples_x, samples_y_aggregation] = inputs
      sys.stderr.write(
          "[%s] DEBUG: Evaluating %dth of %d samples\n"
          % (
              os.path.basename(__file__),
              samples_idx + 1,
              len(samples_x),
          )
      )
      outlier = None
  
      # Create a diagnostic regression model which removes the sample that we
      # want to evaluate
      diagnostic_regressor_gp = gp_create_model.create_model(
          samples_x[0:samples_idx] + samples_x[samples_idx + 1 :],
          samples_y_aggregation[0:samples_idx]
          + samples_y_aggregation[samples_idx + 1 :],
      )
      mu, sigma = gp_prediction.predict(
          samples_x[samples_idx], diagnostic_regressor_gp["model"]
      )
  
      # 2.33 is the z-score for 98% confidence level
      if abs(samples_y_aggregation[samples_idx] - mu) > (2.33 * sigma):
          outlier = {
              "samples_idx": samples_idx,
              "expected_mu": mu,
              "expected_sigma": sigma,
              "difference": abs(samples_y_aggregation[samples_idx] - mu)
              - (2.33 * sigma),
          }
      return outlier
  
file_path: nni/nni/algorithms/hpo/metis_tuner/Regression_GP/OutlierDetection.py
imports: import os
  import sys
  from multiprocessing.dummy import Pool
  from .None import CreateModel
  from .None import Prediction
label_desc: Use image node_id 1 for calling a global function with example usage: _outlierDetection_threaded(inputs) and returns: outlier