text: 
  from sklearn.tree.tree import DecisionTreeClassifier as Op
  import lale.helpers
  import lale.operators
  import lale.docstrings
  from numpy import nan, inf
  
  class DecisionTreeClassifierImpl():
  
      def __init__(self, criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, class_weight='balanced', presort=False):
          self._hyperparams = {
              'criterion': criterion,
              'splitter': splitter,
              'max_depth': max_depth,
              'min_samples_split': min_samples_split,
              'min_samples_leaf': min_samples_leaf,
              'min_weight_fraction_leaf': min_weight_fraction_leaf,
              'max_features': max_features,
              'random_state': random_state,
              'max_leaf_nodes': max_leaf_nodes,
              'min_impurity_decrease': min_impurity_decrease,
              'min_impurity_split': min_impurity_split,
              'class_weight': class_weight,
              'presort': presort}
          self._wrapped_model = Op(**self._hyperparams)
  
      def fit(self, X, y=None):
          if (y is not None):
              self._wrapped_model.fit(X, y)
          else:
              self._wrapped_model.fit(X)
          return self
  
      def predict(self, X):
          return self._wrapped_model.predict(X)
  
      def predict_proba(self, X):
          return self._wrapped_model.predict_proba(X)
  _hyperparams_schema = {
      '$schema': 'http://json-schema.org/draft-04/schema#',
      'description': 'inherited docstring for DecisionTreeClassifier    A decision tree classifier.',
      'allOf': [{
          'type': 'object',
          'required': ['criterion', 'splitter', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_features', 'random_state', 'max_leaf_nodes', 'min_impurity_decrease', 'min_impurity_split', 'class_weight', 'presort'],
          'relevantToOptimizer': ['criterion', 'splitter', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'max_features'],
          'additionalProperties': False,
          'properties': {
              'criterion': {
                  'enum': ['entropy', 'gini'],
                  'default': 'gini',
                  'description': 'The function to measure the quality of a split'},
              'splitter': {
                  'enum': ['random', 'best'],
                  'default': 'best',
                  'description': 'The strategy used to choose the split at each node'},
              'max_depth': {
                  'anyOf': [{
                      'type': 'integer',
                      'minimumForOptimizer': 3,
                      'maximumForOptimizer': 5,
                      'distribution': 'uniform'}, {
                      'enum': [None]}],
                  'default': None,
                  'description': 'The maximum depth of the tree'},
              'min_samples_split': {
                  'anyOf': [{
                      'type': 'integer',
                      'minimumForOptimizer': 2,
                      'maximumForOptimizer': 5,
                      'distribution': 'uniform'}, {
                      'type': 'number',
                      'forOptimizer': False}],
                  'default': 2,
                  'description': 'The minimum number of samples required to split an internal node:  - If int, then consider `min_samples_split` as the minimum number'},
              'min_samples_leaf': {
                  'anyOf': [{
                      'type': 'integer',
                      'minimumForOptimizer': 1,
                      'maximumForOptimizer': 5,
                      'distribution': 'uniform'}, {
                      'type': 'number',
                      'forOptimizer': False}],
                  'default': 1,
                  'description': 'The minimum number of samples required to be at a leaf node'},
              'min_weight_fraction_leaf': {
                  'type': 'number',
                  'default': 0.0,
                  'description': 'The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node'},
              'max_features': {
                  'anyOf': [{
                      'type': 'integer',
                      'forOptimizer': False}, {
                      'type': 'number',
                      'minimumForOptimizer': 0.01,
                      'maximumForOptimizer': 1.0,
                      'distribution': 'uniform'}, {
                      'type': 'string',
                      'forOptimizer': False}, {
                      'enum': [None]}],
                  'default': None,
                  'description': 'The number of features to consider when looking for the best split:      - If int, then consider `max_features` features at each split'},
              'random_state': {
                  'anyOf': [{
                      'type': 'integer'}, {
                      'type': 'object'}, {
                      'enum': [None]}],
                  'default': None,
                  'description': 'If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`.'},
              'max_leaf_nodes': {
                  'anyOf': [{
                      'type': 'integer'}, {
                      'enum': [None]}],
                  'default': None,
                  'description': 'Grow a tree with ``max_leaf_nodes`` in best-first fashion'},
              'min_impurity_decrease': {
                  'type': 'number',
                  'default': 0.0,
                  'description': 'A node will be split if this split induces a decrease of the impurity greater than or equal to this value'},
              'min_impurity_split': {
                  'anyOf': [{
                      'type': 'number'}, {
                      'enum': [None]}],
                  'default': None,
                  'description': 'Threshold for early stopping in tree growth'},
              'class_weight': {
                  'XXX TODO XXX': 'dict, list of dicts, "balanced" or None, default=None',
                  'description': 'Weights associated with classes in the form ``{class_label: weight}``',
                  'enum': ['balanced'],
                  'default': 'balanced'},
              'presort': {
                  'type': 'boolean',
                  'default': False,
                  'description': 'Whether to presort the data to speed up the finding of best splits in fitting'},
          }}, {
          'XXX TODO XXX': 'Parameter: min_samples_leaf > only be considered if it leaves at least min_samples_leaf training samples in each of the left and right branches'}],
  }
  _input_fit_schema = {
      '$schema': 'http://json-schema.org/draft-04/schema#',
      'description': 'Build a decision tree classifier from the training set (X, y).',
      'type': 'object',
      'required': ['X', 'y'],
      'properties': {
          'X': {
              'anyOf': [{
                  'type': 'array',
                  'items': {
                      'laleType': 'Any',
                      'XXX TODO XXX': 'item type'},
                  'XXX TODO XXX': 'array-like or sparse matrix, shape = [n_samples, n_features]'}, {
                  'type': 'array',
                  'items': {
                      'type': 'array',
                      'items': {
                          'type': 'number'},
                  }}],
              'description': 'The training input samples'},
          'y': {
              'anyOf': [{
                  'type': 'array',
                  'items': {
                      'type': 'number'},
              }, {
                  'type': 'array',
                  'items': {
                      'type': 'array',
                      'items': {
                          'type': 'number'},
                  }}],
              'description': 'The target values (class labels) as integers or strings.'},
          'sample_weight': {
              'anyOf': [{
                  'type': 'array',
                  'items': {
                      'type': 'number'},
              }, {
                  'enum': [None]}],
              'description': 'Sample weights'},
          'check_input': {
              'type': 'boolean',
              'default': True,
              'description': 'Allow to bypass several input checking'},
          'X_idx_sorted': {
              'anyOf': [{
                  'type': 'array',
                  'items': {
                      'type': 'array',
                      'items': {
                          'type': 'number'},
                  }}, {
                  'enum': [None]}],
              'default': None,
              'description': 'The indexes of the sorted training input samples'},
      },
  }
  _input_predict_schema = {
      '$schema': 'http://json-schema.org/draft-04/schema#',
      'description': 'Predict class or regression value for X.',
      'type': 'object',
      'required': ['X'],
      'properties': {
          'X': {
              'anyOf': [{
                  'type': 'array',
                  'items': {
                      'laleType': 'Any',
                      'XXX TODO XXX': 'item type'},
                  'XXX TODO XXX': 'array-like or sparse matrix of shape = [n_samples, n_features]'}, {
                  'type': 'array',
                  'items': {
                      'type': 'array',
                      'items': {
                          'type': 'number'},
                  }}],
              'description': 'The input samples'},
          'check_input': {
              'type': 'boolean',
              'default': True,
              'description': 'Allow to bypass several input checking'},
      },
  }
  _output_predict_schema = {
      '$schema': 'http://json-schema.org/draft-04/schema#',
      'description': 'The predicted classes, or the predict values.',
      'anyOf': [{
          'type': 'array',
          'items': {
              'type': 'number'},
      }, {
          'type': 'array',
          'items': {
              'type': 'array',
              'items': {
                  'type': 'number'},
          }}],
  }
  _input_predict_proba_schema = {
      '$schema': 'http://json-schema.org/draft-04/schema#',
      'description': 'Predict class probabilities of the input samples X.',
      'type': 'object',
      'required': ['X'],
      'properties': {
          'X': {
              'anyOf': [{
                  'type': 'array',
                  'items': {
                      'laleType': 'Any',
                      'XXX TODO XXX': 'item type'},
                  'XXX TODO XXX': 'array-like or sparse matrix of shape = [n_samples, n_features]'}, {
                  'type': 'array',
                  'items': {
                      'type': 'array',
                      'items': {
                          'type': 'number'},
                  }}],
              'description': 'The input samples'},
          'check_input': {
              'type': 'boolean',
              'description': 'Run check_array on X.'},
      },
  }
  _output_predict_proba_schema = {
      '$schema': 'http://json-schema.org/draft-04/schema#',
      'description': 'such arrays if n_outputs > 1',
      'laleType': 'Any',
      'XXX TODO XXX': 'array of shape = [n_samples, n_classes], or a list of n_outputs',
  }
  _combined_schemas = {
      '$schema': 'http://json-schema.org/draft-04/schema#',
      'description': 'Combined schema for expected data and hyperparameters.',
      'documentation_url': 'https://scikit-learn.org/0.20/modules/generated/sklearn.tree.DecisionTreeClassifier#sklearn-tree-decisiontreeclassifier',
      'type': 'object',
      'tags': {
          'pre': [],
          'op': ['estimator', 'classifier'],
          'post': []},
      'properties': {
          'hyperparams': _hyperparams_schema,
          'input_fit': _input_fit_schema,
          'input_predict': _input_predict_schema,
          'output_predict': _output_predict_schema,
          'input_predict_proba': _input_predict_proba_schema,
          'output_predict_proba': _output_predict_proba_schema},
  }
  lale.docstrings.set_docstrings(DecisionTreeClassifierImpl, _combined_schemas)
  DecisionTreeClassifier = lale.operators.make_operator(DecisionTreeClassifierImpl, _combined_schemas)
  
  