code: def mspe(pred, gt):
      """
      Desc:
          Mean Square Percentage Error
      Args:
          pred:
          gt: ground truth vector
      Returns:
          MSPE value
      """
      return (
          np.mean(np.square((pred - gt) / gt))
          if len(pred) > 0 and len(gt) > 0
          else 0
      )
  
file_path: PGL/examples/kddcup2022/wpf_baseline/metrics.py
imports: import pandas
  import time
  import numpy
label_desc: Use image node_id 8 for calling a global function with example usage: mspe(pred, gt) and returns: unknown