code: def predict(self, X, check_input=True):
      ivy.seed(seed_value=self.random_state)
      proba = self.tree_.predict(X)
      n_samples = X.shape[0]
  
      # Classification
  
      if self.n_outputs_ == 1:
          return ivy.gather(
              self.classes_, ivy.argmax(proba, axis=1), axis=0
          )
  
      else:
          class_type = self.classes_[0].dtype
          predictions = ivy.zeros(
              (n_samples, self.n_outputs_), dtype=class_type
          )
          for k in range(self.n_outputs_):
              predictions[:, k] = ivy.gather(
                  self.classes_[k],
                  ivy.argmax(proba[:, k], axis=1),
                  axis=0,
              )
  
          return predictions
  
imports: from abc import ABCMeta, abstractmethod
  from ..base import BaseEstimator, ClassifierMixin, MultiOutputMixin
  import copy
  from ._criterion import Gini, Criterion
  from ._splitter import BestSplitter, Splitter
  from ._tree import DepthFirstTreeBuilder, Tree
  import ivy
  import numbers
label_desc: Use image node_id 6 for calling the BaseDecisionTree obj's underlying member method code with example usage: obj.predict(X, check_input) and returns: ivy, predictions