code: def _validate_for_predict(self, X):
      # check_is_fitted(self, 'support_')
      sparse = sp.isspmatrix(X)
      self._sparse = sparse and not callable(self.kernel)
      X = check_array(
          X, accept_sparse="csr", dtype=np.float64, order="C"
      )
      if self._sparse and not sp.isspmatrix(X):
          X = sp.csr_matrix(X)
      if self._sparse:
          X.sort_indices()
  
      if (
          sp.issparse(X)
          and not self._sparse
          and not callable(self.kernel)
      ):
          raise ValueError(
              "cannot use sparse input in %r trained on dense data"
              % type(self).__name__
          )
  
      return X
  
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 6 for calling the SvmModel obj's underlying member method code with example usage: obj._validate_for_predict(X) and returns: X