code: def _handle_missing(data, groups, formula, re_formula, vc_formula):
      tokens = set()
  
      forms = [formula]
      if re_formula is not None:
          forms.append(re_formula)
      if vc_formula is not None:
          forms.extend(vc_formula.values())
  
      from statsmodels.compat.python import asunicode
  
      from io import StringIO
      import tokenize
  
      skiptoks = {"(", ")", "*", ":", "+", "-", "**", "/"}
  
      for fml in forms:
          # Unicode conversion is for Py2 compatability
          rl = StringIO(fml)
  
          def rlu():
              line = rl.readline()
              return asunicode(line, "ascii")
  
          g = tokenize.generate_tokens(rlu)
          for tok in g:
              if tok not in skiptoks:
                  tokens.add(tok.string)
      tokens = sorted(tokens & set(data.columns))
  
      data = data[tokens]
      ii = pd.notnull(data).all(1)
      if type(groups) is not str:
          ii &= pd.notnull(groups)
  
      return data.loc[ii, :], groups[np.asarray(ii)]
  
imports: import warnings
  import numpy
  import pandas
  import patsy
  from scipy import sparse
  from scipy.stats.distributions import norm
  from statsmodels.base._penalties import Penalty
  import statsmodels.base.model
  from statsmodels.tools import data
  from statsmodels.tools.decorators import cache_readonly
  from statsmodels.tools.sm_exceptions import ConvergenceWarning
label_desc: Use image node_id 8 for calling a global function with example usage: _handle_missing(data, groups, formula, re_formula, vc_formula) and returns: data,  groups, asunicode