code: def calc_hyper_param_importance(df, hyper_param, target):
      new_df = df[[hyper_param, target]]
      no_missing_value_df = new_df.dropna()
  
      # Can not calc pearson correlation coefficient when number of samples is less or equal than 2
      if len(no_missing_value_df) <= 2:
          logger.error("Number of samples is less or equal than 2.")
          return 0
  
      correlation = no_missing_value_df[target].corr(
          no_missing_value_df[hyper_param]
      )
      if np.isnan(correlation):
          logger.warning("Correlation is nan!")
          return 0
  
      return abs(correlation)
  
imports: from functools import reduce
  import numpy
  import pandas
  from visualdl.server.log import logger
label_desc: Use image node_id 1 for calling a global function with example usage: calc_hyper_param_importance(df, hyper_param, target) and returns: abs, int, int