docstr: A simple pure python wrapper around a GLC decision tree object.
  
  The tree can be obtained directly from trees_json parameter in any of
  GLC model objects (boosted trees, random forests, and decision trees).
  
  Parameters
  ----------
  model : Tree/Tree ensemble model. Can be any tree of type boosted
          trees, random forests, or decision tree.
  
  tree_id : Tree id in the ensemble to export.
  
  Examples
  --------
  .. sourcecode:: python
  
      >>> url = 'https://static.turi.com/datasets/xgboost/mushroom.csv'
      >>> data = tc.SFrame.read_csv(url)
  
      >>> train, test = data.random_split(0.8)
      >>> model = tc.boosted_trees_classifier.create(train,
      ...                  target='label', validation_set = None)
  
      >>> tree = model._get_tree()
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code: def __init__(self):
      """
      A simple pure python wrapper around a GLC decision tree object.
  
      The tree can be obtained directly from trees_json parameter in any of
      GLC model objects (boosted trees, random forests, and decision trees).
  
      Parameters
      ----------
      model : Tree/Tree ensemble model. Can be any tree of type boosted
              trees, random forests, or decision tree.
  
      tree_id : Tree id in the ensemble to export.
  
      Examples
      --------
      .. sourcecode:: python
  
          >>> url = 'https://static.turi.com/datasets/xgboost/mushroom.csv'
          >>> data = tc.SFrame.read_csv(url)
  
          >>> train, test = data.random_split(0.8)
          >>> model = tc.boosted_trees_classifier.create(train,
          ...                  target='label', validation_set = None)
  
          >>> tree = model._get_tree()
      """
      pass
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  from turicreate.util import _raise_error_if_not_of_type
  from turicreate.toolkits._internal_utils import _numeric_param_check_range
  import sys
label_desc: Use image node_id 1 to create a new DecisionTree object  with example: obj = DecisionTree()