code: def _get_covariance(model, robust):
      if robust is None:
          return model.cov_params()
      elif robust == "hc0":
          return model.cov_HC0
      elif robust == "hc1":
          return model.cov_HC1
      elif robust == "hc2":
          return model.cov_HC2
      elif robust == "hc3":
          return model.cov_HC3
      else:  # pragma: no cover
          raise ValueError("robust options %s not understood" % robust)
  
imports: from statsmodels.compat.python import lrange
  import numpy
  import pandas
  from pandas import DataFrame, Index
  import patsy
  from scipy import stats
  from statsmodels.formula.formulatools import _has_intercept, _intercept_idx, _remove_intercept_patsy
  from statsmodels.iolib import summary2
  from statsmodels.regression.linear_model import OLS
label_desc: Use image node_id 1 for calling a global function with example usage: _get_covariance(model, robust) and returns: model, model, model, model, model