code: def _dense_decision_function(self, X):
      X = check_array(X, dtype=np.float64, order="C")
      samples = X.shape[0]
      features = X.shape[1]
      X_1d = X.ravel()
  
      data = (c_float * X_1d.size)()
      data[:] = X_1d
      dec_size = X.shape[0] * self.n_binary_model
      dec_value_ptr = (c_float * dec_size)()
      thundersvm.dense_decision(
          samples,
          features,
          data,
          c_void_p(self.model),
          dec_size,
          dec_value_ptr,
      )
      self.dec_values = (
          np.frombuffer(dec_value_ptr, dtype=np.float32)
          .astype(float)
          .reshape((X.shape[0], self.n_binary_model))
      )
      return self.dec_values
  
imports: from sklearn.base import BaseEstimator
  from sklearn.base import RegressorMixin, ClassifierMixin
  import numpy
  import scipy.sparse
  from sklearn.utils import check_X_y, column_or_1d, check_array
  from sklearn.utils.validation import _num_samples
  from ctypes import *
  from os import path, curdir
  from sys import platform
label_desc: Use image node_id 12 for calling the SvmModel obj's underlying member method code with example usage: obj._dense_decision_function(X) and returns: self