code: def _make_exog(result, focus_var, summaries, values, num_points):
      # Branch depending on whether the model was fit with a formula.
      if hasattr(result.model.data, "frame"):
          dexog, fexog, fvals = _make_exog_from_formula(
              result, focus_var, summaries, values, num_points
          )
      else:
          exog, fvals = _make_exog_from_arrays(
              result, focus_var, summaries, values, num_points
          )
          dexog, fexog = exog, exog
  
      return dexog, fexog, fvals
  
file_path: statsmodels/statsmodels/sandbox/predict_functional.py
imports: import pandas
  import patsy
  import numpy
  import warnings
  from statsmodels.tools.sm_exceptions import ValueWarning
  from statsmodels.compat.pandas import Appender
label_desc: Use image node_id 3 for calling a global function with example usage: _make_exog(result, focus_var, summaries, values, num_points) and returns: dexog,  fexog,  fvals