code: def _validate_dual_parameter(dual, loss, penalty, multi_class, X):
      """Helper function to assign the value of dual parameter."""
      if dual == "auto":
          if X.shape[0] < X.shape[1]:
              try:
                  _get_liblinear_solver_type(
                      multi_class, penalty, loss, True
                  )
                  return True
              except (
                  ValueError
              ):  # dual not supported for the combination
                  return False
          else:
              try:
                  _get_liblinear_solver_type(
                      multi_class, penalty, loss, False
                  )
                  return False
              except (
                  ValueError
              ):  # primal not supported by the combination
                  return True
      # TODO 1.5
      elif dual == "warn":
          warnings.warn(
              (
                  "The default value of `dual` will change from `True` to `'auto'` in"
                  " 1.5. Set the value of `dual` explicitly to suppress the warning."
              ),
              FutureWarning,
          )
          return True
      else:
          return dual
  
imports: import warnings
  from numbers import Integral, Real
  import numpy
  from ..base import BaseEstimator, OutlierMixin, RegressorMixin, _fit_context
  from ..linear_model._base import LinearClassifierMixin, LinearModel, SparseCoefMixin
  from ..utils import deprecated
  from ..utils._param_validation import Hidden, Interval, StrOptions
  from ..utils.multiclass import check_classification_targets
  from ..utils.validation import _num_samples
  from ._base import BaseLibSVM, BaseSVC, _fit_liblinear, _get_liblinear_solver_type
label_desc: Use image node_id 1 for calling a global function with example usage: _validate_dual_parameter(dual, loss, penalty, multi_class, X) and returns: True, dual, True, False, False, True