code: def MAPE(actual, pred):
      """
      Calculate mean absolute percentage error.
      Remove NA and values where actual is close to zero
      """
      not_na = ~(np.isnan(actual) | np.isnan(pred))
      not_zero = ~np.isclose(actual, 0.0)
      actual_safe = actual[not_na & not_zero]
      pred_safe = pred[not_na & not_zero]
      return np.mean(APE(actual_safe, pred_safe))
  
file_path: MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-github-dau/infer.py
imports: import argparse
  import os
  import numpy
  import pandas
  import joblib
  from sklearn.metrics import mean_absolute_error, mean_squared_error
  from azureml.automl.runtime.shared.score import scoring, constants
  from azureml.core import Run
label_desc: Use image node_id 3 for calling a global function with example usage: MAPE(actual, pred) and returns: np