code: def compute_accuracy(candidate_list, reference_list, form_manager):
      if len(candidate_list) != len(reference_list):
          print(
              "candidate list has length {}, reference list has length {}\n".format(
                  len(candidate_list), len(reference_list)
              )
          )
      len_min = min(len(candidate_list), len(reference_list))
      c = 0
      for i in range(len_min):
          if is_all_same(
              candidate_list[i], reference_list[i], form_manager
          ):
              c = c + 1
          else:
              pass
      return c / float(len_min)
  
file_path: graph4nlp/examples/pytorch/math_word_problem/mawps/src/evaluation.py
imports: from graph4nlp.pytorch.modules.evaluation.base import EvaluationMetricBase
  import sympy
  from sympy.parsing.sympy_parser import parse_expr
label_desc: Use image node_id 4 for calling a global function with example usage: compute_accuracy(candidate_list, reference_list, form_manager) and returns: unknown