code: def chi_square_test(observed, expected, alpha=0.05, df=None):
      """Testing Goodness-of-fit Test with Pearson's Chi-squared Test.
  
      Args:
          observed (list of ints): List of # of counts each element is observed.
          expected (list of floats): List of # of counts each element is expected
              to be observed.
          alpha (float): Significance level. Currently,
              only 0.05 and 0.01 are acceptable.
          df (int): Degree of freedom. If ``None``,
              it is set to the length of ``observed`` minus 1.
  
      Returns:
          bool: ``True`` if null hypothesis is **NOT** reject.
          Otherwise, ``False``.
      """
      if df is None:
          df = observed.size - 1
  
      if alpha == 0.01:
          alpha_idx = 0
      elif alpha == 0.05:
          alpha_idx = 1
      else:
          raise ValueError("support only alpha == 0.05 or 0.01")
  
      chi_square = numpy.sum((observed - expected) ** 2 / expected)
      return chi_square < chi_square_table[alpha_idx][df]
  
label_desc: Use image node_id 1 for calling a global function with example usage: chi_square_test(observed, expected, alpha, df) and returns: unknown