code: def extract_dual_coef(
      num_classes, sv_ind_by_clf, sv_coef_by_clf, labels
  ):
      """Construct dual coefficients array in SKLearn peculiar layout,
      as well corresponding support vector indexes
      """
      sv_ind_by_class = group_indices_by_class(
          num_classes, sv_ind_by_clf, labels
      )
      sv_ind_mapping = map_sv_to_columns_in_dual_coef_matrix(
          sv_ind_by_class
      )
  
      num_unique_sv = len(sv_ind_mapping)
      dc_dt = sv_coef_by_clf[0].dtype
  
      dual_coef = np.zeros(
          (num_classes - 1, num_unique_sv), dtype=dc_dt
      )
      support_ = np.empty((num_unique_sv,), dtype=np.int32)
  
      p = 0
      for i in range(0, num_classes):
          for j in range(i + 1, num_classes):
              sv_ind_i_vs_j = sv_ind_by_clf[p]
              sv_coef_i_vs_j = sv_coef_by_clf[p]
              p += 1
  
              for k, sv_index in enumerate(sv_ind_i_vs_j):
                  label = labels[sv_index]
                  col_index = sv_ind_mapping[sv_index]
                  if j == label:
                      row_index = i
                  else:
                      row_index = j - 1
                  dual_coef[row_index, col_index] = sv_coef_i_vs_j[k]
                  support_[col_index] = sv_index
  
      return dual_coef, support_
  
imports: from __future__ import print_function
  import warnings
  import numpy
  import sklearn.svm._base
  import sklearn.svm._classes
  from scipy import sparse
  from sklearn.calibration import CalibratedClassifierCV
  from sklearn.exceptions import NotFittedError
  from sklearn.model_selection import StratifiedKFold
  from sklearn.utils import check_random_state
  from sklearn.utils.multiclass import _ovr_decision_function
  from sklearn.utils.validation import _check_sample_weight, _num_samples, check_consistent_length, check_is_fitted
  from sklearn import __version__
  import daal4py
  from .._utils import PatchingConditionsChain, getFPType, make2d, sklearn_check_version
label_desc: Use image node_id 10 for calling a global function with example usage: extract_dual_coef(num_classes, sv_ind_by_clf, sv_coef_by_clf, labels) and returns: dual_coef,  support_