code: def predict(self, X, pred_contribs=False, pred_interactions=False):
      # Input validation
      if not self.allow_nan_:
          X = check_array(X, dtype=[np.single, np.double])
      else:
          X = check_array(
              X,
              dtype=[np.single, np.double],
              force_all_finite="allow-nan",
          )
  
      # Check is fit had been called
      check_is_fitted(self, ["n_features_in_"])
  
      fptype = getFPType(X)
      return self._predict_regression(
          X, fptype, pred_contribs, pred_interactions
      )
  
imports: import numbers
  import numpy
  from sklearn import preprocessing
  from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin
  from sklearn.utils import check_random_state
  from sklearn.utils.multiclass import check_classification_targets
  from sklearn.utils.validation import check_array, check_is_fitted, check_X_y
  import daal4py
  from .._utils import control_n_jobs, getFPType, run_with_n_jobs
label_desc: Use image node_id 2 for calling the GBTDAALRegressor obj's underlying member method code with example usage: obj.predict(X, pred_contribs, pred_interactions) and returns: self