How to use the lale.docstrings function in lale

self._wrapped_model = sklearn.tree.tree.DecisionTreeClassifier(**self._hyperparams)

    def fit(self, X, y, **fit_params):
        if fit_params is None:
            self._wrapped_model.fit(X, y)
        else:
            self._wrapped_model.fit(X, y, **fit_params)
        return self

    def predict(self, X):
        return self._wrapped_model.predict(X)

    def predict_proba(self, X):
        return self._wrapped_model.predict_proba(X)

lale.docstrings.set_docstrings(DecisionTreeClassifierImpl, _combined_schemas)

DecisionTreeClassifier = lale.operators.make_operator(DecisionTreeClassifierImpl, _combined_schemas)

'description': """Operator from `autoai_libs`_. Feature transformation via a general wrapper that can be used for most functions (may not be most efficient though).

.. _`autoai_libs`: https://pypi.org/project/autoai-libs""",
    'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.autoai_libs.ta1.html',
    'type': 'object',
    'tags': {
        'pre': [],
        'op': ['transformer'],
        'post': []},
    'properties': {
        'hyperparams': _hyperparams_schema,
        'input_fit': _input_fit_schema,
        'input_transform': _input_transform_schema,
        'output_transform': _output_transform_schema}}

lale.docstrings.set_docstrings(TGenImpl, _combined_schemas)

TGen = lale.operators.make_operator(TGenImpl, _combined_schemas)

'op': ['transformer', 'estimator', 'resampler'],#transformer and estimator both as a higher-order operator
    'post': []},
  'properties': {
    'hyperparams': _hyperparams_schema,
    'input_fit': _input_fit_schema,
    'input_transform': _input_transform_schema,
    'output_transform': _output_transform_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,
    'input_decision_function': _input_decision_function_schema,
    'output_decision_function': _output_decision_function_schema
}}

lale.docstrings.set_docstrings(SMOTEENNImpl, _combined_schemas)

SMOTEENN = lale.operators.make_operator(SMOTEENNImpl, _combined_schemas)

'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.linear_svc.html',
    '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_decision_function': _input_decision_function_schema,
        'output_decision_function': _output_decision_function_schema,
}}

lale.docstrings.set_docstrings(LinearSVCImpl, _combined_schemas)

LinearSVC = lale.operators.make_operator(LinearSVCImpl, _combined_schemas)

.. _`autoai_libs`: https://pypi.org/project/autoai-libs
.. _SimpleImputer: https://scikit-learn.org/0.20/modules/generated/sklearn.impute.SimpleImputer.html#sklearn-impute-simpleimputer""",
    'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.autoai_libs.cat_imputer.html',
    'type': 'object',
    'tags': {
        'pre': [],
        'op': ['transformer'],
        'post': []},
    'properties': {
        'hyperparams': _hyperparams_schema,
        'input_fit': _input_fit_schema,
        'input_transform': _input_transform_schema,
        'output_transform': _output_transform_schema}}

lale.docstrings.set_docstrings(CatImputerImpl, _combined_schemas)

CatImputer = lale.operators.make_operator(CatImputerImpl, _combined_schemas)

_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.decomposition.SparsePCA#sklearn-decomposition-sparsepca',
    'type': 'object',
    'tags': {
        'pre': [],
        'op': ['transformer'],
        'post': []},
    'properties': {
        'hyperparams': _hyperparams_schema,
        'input_fit': _input_fit_schema,
        'input_transform': _input_transform_schema,
        'output_transform': _output_transform_schema},
}
lale.docstrings.set_docstrings(SparsePCAImpl, _combined_schemas)
SparsePCA = lale.operators.make_operator(SparsePCAImpl, _combined_schemas)

.. _`Decision tree regressor`: https://scikit-learn.org/0.20/modules/generated/sklearn.tree.DecisionTreeRegressor.html#sklearn-tree-decisiontreeregressor
""",
    'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.decision_tree_regressor.html',
    'type': 'object',
    'tags': {
        'pre': [],
        'op': ['estimator', 'regressor'],
        'post': []},
    'properties': {
        'hyperparams': _hyperparams_schema,
        'input_fit': _input_fit_schema,
        'input_predict': _input_predict_schema,
        'output_predict': _output_predict_schema}}

lale.docstrings.set_docstrings(DecisionTreeRegressorImpl, _combined_schemas)

DecisionTreeRegressor = lale.operators.make_operator(DecisionTreeRegressorImpl, _combined_schemas)

'description': 'Combined schema for expected data and hyperparameters.',
    'documentation_url': 'https://scikit-learn.org/0.20/modules/generated/sklearn.cluster.MiniBatchKMeans#sklearn-cluster-minibatchkmeans',
    'type': 'object',
    'tags': {
        'pre': [],
        'op': ['transformer', 'estimator'],
        'post': []},
    'properties': {
        'hyperparams': _hyperparams_schema,
        'input_fit': _input_fit_schema,
        'input_transform': _input_transform_schema,
        'output_transform': _output_transform_schema,
        'input_predict': _input_predict_schema,
        'output_predict': _output_predict_schema},
}
lale.docstrings.set_docstrings(MiniBatchKMeansImpl, _combined_schemas)
MiniBatchKMeans = lale.operators.make_operator(MiniBatchKMeansImpl, _combined_schemas)

.. _`Missing values indicator`: https://scikit-learn.org/0.20/modules/generated/sklearn.impute.MissingIndicator.html#sklearn-impute-missingindicator
""",
    'documentation_url': 'https://lale.readthedocs.io/en/latest/modules/lale.lib.sklearn.missing_indicator.html',
    'type': 'object',
    'tags': {
        'pre': [],
        'op': ['transformer'],
        'post': []},
    'properties': {
        'hyperparams': _hyperparams_schema,
        'input_fit': _input_fit_schema,
        'input_transform': _input_transform_schema,
        'output_transform': _output_transform_schema}}

lale.docstrings.set_docstrings(MissingIndicatorImpl, _combined_schemas)

MissingIndicator = lale.operators.make_operator(MissingIndicatorImpl, _combined_schemas)

'description': 'Combined schema for expected data and hyperparameters.',
    'documentation_url': 'https://scikit-learn.org/0.20/modules/generated/sklearn.naive_bayes.GaussianNB#sklearn-naive_bayes-gaussiannb',
    'type': 'object',
    'tags': {
        'pre': [],
        'op': ['estimator'],
        '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(GaussianNBImpl, _combined_schemas)
GaussianNB = lale.operators.make_operator(GaussianNBImpl, _combined_schemas)