code: def convert_name(ind, shap_values, input_names):
      if isinstance(ind, str):
          nzinds = np.where(np.array(input_names) == ind)[0]
          if len(nzinds) == 0:
              # we allow rank based indexing using the format "rank(int)"
              if ind.startswith("rank("):
                  return np.argsort(-np.abs(shap_values).mean(0))[
                      int(ind[5:-1])
                  ]
  
              # we allow the sum of all the SHAP values to be specified with "sum()"
              # assuming here that the calling method can deal with this case
              elif ind == "sum()":
                  return "sum()"
              else:
                  raise ValueError(
                      "Could not find feature named: " + ind
                  )
          else:
              return nzinds[0]
      else:
          return ind
  
imports: import copy
  import os
  import re
  import sys
  from contextlib import contextmanager
  import numpy
  import pandas
  import scipy.special
  import sklearn
label_desc: Use image node_id 4 for calling a global function with example usage: convert_name(ind, shap_values, input_names) and returns: ind, nzinds, np, str