class_docstr: Class responsible for handling input data and extracting metadata into the
  appropriate form
class_docstr_tok: ["Class","responsible","for","handling","input","data","and","extracting","metadata","into","the","appropriate","form"]
code: def _handle_constant(self, hasconst):
      if hasconst is False or self.exog is None:
          self.k_constant = 0
          self.const_idx = None
      else:
          # detect where the constant is
          check_implicit = False
          exog_max = np.max(self.exog, axis=0)
          if not np.isfinite(exog_max).all():
              raise MissingDataError("exog contains inf or nans")
          exog_min = np.min(self.exog, axis=0)
          const_idx = np.where(exog_max == exog_min)[0].squeeze()
          self.k_constant = const_idx.size
  
          if self.k_constant == 1:
              if self.exog[:, const_idx].mean() != 0:
                  self.const_idx = int(const_idx)
              else:
                  # we only have a zero column and no other constant
                  check_implicit = True
          elif self.k_constant > 1:
              # we have more than one constant column
              # look for ones
              values = []  # keep values if we need != 0
              for idx in const_idx:
                  value = self.exog[:, idx].mean()
                  if value == 1:
                      self.k_constant = 1
                      self.const_idx = int(idx)
                      break
                  values.append(value)
              else:
                  # we did not break, no column of ones
                  pos = np.array(values) != 0
                  if pos.any():
                      # take the first nonzero column
                      self.k_constant = 1
                      self.const_idx = int(const_idx[pos.argmax()])
                  else:
                      # only zero columns
                      check_implicit = True
          elif self.k_constant == 0:
              check_implicit = True
          else:
              # should not be here
              pass
  
          if check_implicit and not hasconst:
              # look for implicit constant
              # Compute rank of augmented matrix
              augmented_exog = np.column_stack(
                  (np.ones(self.exog.shape[0]), self.exog)
              )
              rank_augm = np.linalg.matrix_rank(augmented_exog)
              rank_orig = np.linalg.matrix_rank(self.exog)
              self.k_constant = int(rank_orig == rank_augm)
              self.const_idx = None
          elif hasconst:
              # Ensure k_constant is 1 any time hasconst is True
              # even if one is not found
              self.k_constant = 1
  
imports: from __future__ import annotations
  from statsmodels.compat.python import lmap
  from functools import reduce
  import numpy
  from pandas import DataFrame, Series, isnull, MultiIndex
  import statsmodels.tools.data
  from statsmodels.tools.decorators import cache_readonly, cache_writable
  from statsmodels.tools.sm_exceptions import MissingDataError
label_desc: Use image node_id 4 for calling the ModelData obj's underlying member method code with example usage: obj._handle_constant(hasconst) without return types