code: def _transform_predict_exog(model, exog, design_info=None):
      """transform exog for predict using design_info
  
      Note: this is copied from base.model.Results.predict and converted to
      standalone function with additional options.
      """
  
      is_pandas = _is_using_pandas(exog, None)
  
      exog_index = exog.index if is_pandas else None
  
      if design_info is None:
          design_info = getattr(model.data, "design_info", None)
  
      if design_info is not None and (exog is not None):
          from patsy import dmatrix
  
          if isinstance(exog, pd.Series):
              # we are guessing whether it should be column or row
              if (
                  hasattr(exog, "name")
                  and isinstance(exog.name, str)
                  and exog.name in design_info.describe()
              ):
                  # assume we need one column
                  exog = pd.DataFrame(exog)
              else:
                  # assume we need a row
                  exog = pd.DataFrame(exog).T
          orig_exog_len = len(exog)
          is_dict = isinstance(exog, dict)
          exog = dmatrix(design_info, exog, return_type="dataframe")
          if orig_exog_len > len(exog) and not is_dict:
              import warnings
  
              if exog_index is None:
                  warnings.warn(
                      "nan values have been dropped", ValueWarning
                  )
              else:
                  exog = exog.reindex(exog_index)
          exog_index = exog.index
  
      if exog is not None:
          exog = np.asarray(exog)
          if exog.ndim == 1 and (
              model.exog.ndim == 1 or model.exog.shape[1] == 1
          ):
              exog = exog[:, None]
          exog = np.atleast_2d(exog)  # needed in count model shape[1]
  
      return exog, exog_index
  
imports: from collections.abc import Iterable
  import copy
  import numpy
  from scipy import optimize
  import pandas
  import statsmodels.base.wrapper
  from statsmodels.discrete.discrete_model import Logit
  from statsmodels.genmod.generalized_linear_model import GLM, GLMResults, GLMResultsWrapper, _check_convergence
  import statsmodels.regression.linear_model
  from statsmodels.tools.sm_exceptions import PerfectSeparationError, ValueWarning
  from statsmodels.tools.decorators import cache_readonly
  from statsmodels.tools.data import _is_using_pandas
  from statsmodels.tools.linalg import matrix_sqrt
  from statsmodels.base._penalized import PenalizedMixin
  from statsmodels.gam.gam_penalties import MultivariateGamPenalty
  from statsmodels.gam.gam_cross_validation.gam_cross_validation import MultivariateGAMCVPath
  from statsmodels.gam.gam_cross_validation.cross_validators import KFold
label_desc: Use image node_id 1 for calling a global function with example usage: _transform_predict_exog(model, exog, design_info) and returns: exog,  exog_index