code: def _daal_predict_classifier(self, X):
      X_fptype = getFPType(X)
      dfc_algorithm = daal4py.decision_forest_classification_prediction(
          nClasses=int(self.n_classes_),
          fptype=X_fptype,
          resultsToEvaluate="computeClassLabels",
      )
      if X.shape[1] != self.n_features_in_:
          raise ValueError(
              (
                  f"X has {X.shape[1]} features, "
                  f"but RandomForestClassifier is expecting "
                  f"{self.n_features_in_} features as input"
              )
          )
      dfc_predictionResult = dfc_algorithm.compute(X, self.daal_model_)
  
      pred = dfc_predictionResult.prediction
  
      return np.take(
          self.classes_, pred.ravel().astype(np.int64, casting="unsafe")
      )
  
imports: import logging
  import numbers
  import warnings
  from math import ceil
  import numpy
  from scipy import sparse
  from sklearn.base import clone
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.exceptions import DataConversionWarning
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from sklearn.tree._tree import Tree
  from sklearn.utils import check_array, check_random_state, deprecated
  from sklearn.utils.validation import _num_samples, check_consistent_length, check_is_fitted
  import daal4py
  from daal4py.sklearn._utils import PatchingConditionsChain, check_tree_nodes, control_n_jobs, daal_check_version, getFPType, run_with_n_jobs, sklearn_check_version
  from .._device_offload import support_usm_ndarray
  from ..utils.validation import _daal_num_features
label_desc: Use image node_id 7 for calling the RandomForestClassifier obj's underlying member method code with example usage: obj._daal_predict_classifier(X) and returns: np