How to use the xgboost.__version__ function in xgboost

def get_estimators(frameworks, model_types, objective_type):
    # frameworks: ["xgboost", "lightgbm", "sklearn", "catboost", "tf", "keras", "fastai", "all"]
    # model types: ["tree_based", "linear", "deep_learning"]
    xgb_min_ver = "0.9"
    lgb_min_ver = "2.3.1"
    sklearn_min_ver = "0.22"
    # TODO: add exclude frameworks
    estimators = []
    for framework in frameworks:
        if str.startswith(framework, "xgb"):
            try:
                import xgboost as xgb
                xgb_ver = xgb.__version__
                if ge_version(xgb_ver, xgb_min_ver):
                    if any(item.startswith('linear') for item in model_types):
                        if objective_type == "classification":
                            estimators.append("XGBLinearClassifier")
                        else:
                            estimators.append("XGBLinearRegressor")
                    if any(item.startswith('tree') for item in model_types):
                        if objective_type == "classification":
                            estimators.append("XGBTreeClassifier")
                        else:
                            estimators.append("XGBTreeRegressor")
            except:
                # TODO: log that xgboost is not installed in the right version
                continue

        if str.startswith(framework, ("lgb", "lightgbm")):

def get_default_conda_env():
    """
    :return: The default Conda environment for MLflow Models produced by calls to
             :func:`save_model()` and :func:`log_model()`.
    """
    import xgboost as xgb

    return _mlflow_conda_env(
        additional_conda_deps=None,
        # XGBoost is not yet available via the default conda channels, so we install it via pip
        additional_pip_deps=[
            "xgboost=={}".format(xgb.__version__),
        ],
        additional_conda_channels=None)

def __init__(self, params):
        super(XgbLearner, self).__init__(params)
        self.library_version = xgb.__version__
        self.model_file = self.uid + ".xgb.model"
        self.model_file_path = os.path.join(storage_path, self.model_file)

        self.boosting_rounds = additional.get(
            "one_step", 50
        )  # params.get("boosting_rounds", 50)
        self.max_iters = additional.get("max_steps", 3)
        self.learner_params = {
            "booster": self.params.get("booster", "gbtree"),
            "objective": self.params.get("objective"),
            "eval_metric": self.params.get("eval_metric"),
            "eta": self.params.get("eta", 0.01),
            "max_depth": self.params.get("max_depth", 1),
            "min_child_weight": self.params.get("min_child_weight", 1),
            "subsample": self.params.get("subsample", 0.8),
            "colsample_bytree": self.params.get("colsample_bytree", 0.8),

MOCK_MODULES = ['scipy', 'scipy.sparse', 'sklearn', 'pandas']
for mod_name in MOCK_MODULES:
  sys.modules[mod_name] = mock.Mock()

# -- General configuration ------------------------------------------------

# General information about the project.
project = u'xgboost'
author = u'%s developers' % project
copyright = u'2019, %s' % author
github_doc_root = 'https://github.com/dmlc/xgboost/tree/master/doc/'

os.environ['XGBOOST_BUILD_DOC'] = '1'
# Version information.
import xgboost
version = xgboost.__version__
release = xgboost.__version__

# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones
extensions = [
    'matplotlib.sphinxext.plot_directive',
    'sphinx.ext.autodoc',
    'sphinx.ext.napoleon',
    'sphinx.ext.mathjax',
    'sphinx.ext.intersphinx',
    'breathe'
]

graphviz_output_format = 'png'
plot_formats = [('svg', 300), ('png', 100), ('hires.png', 300)]
plot_html_show_source_link = False

# General information about the project.
project = u'xgboost'
author = u'%s developers' % project
copyright = u'2015, %s' % author
github_doc_root = 'https://github.com/dmlc/xgboost/tree/master/doc/'

# add markdown parser
MarkdownParser.github_doc_root = github_doc_root
source_parsers = {
    '.md': MarkdownParser,
}
os.environ['XGBOOST_BUILD_DOC'] = '1'
# Version information.
import xgboost
version = xgboost.__version__
release = xgboost.__version__

# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones
extensions = [
    'sphinx.ext.autodoc',
    'sphinx.ext.napoleon',
    'sphinx.ext.mathjax',
]

# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']

# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
# source_suffix = ['.rst', '.md']
source_suffix = ['.rst', '.md']

for mod_name in MOCK_MODULES:
  sys.modules[mod_name] = mock.Mock()

# -- General configuration ------------------------------------------------

# General information about the project.
project = u'xgboost'
author = u'%s developers' % project
copyright = u'2019, %s' % author
github_doc_root = 'https://github.com/dmlc/xgboost/tree/master/doc/'

os.environ['XGBOOST_BUILD_DOC'] = '1'
# Version information.
import xgboost
version = xgboost.__version__
release = xgboost.__version__

# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom ones
extensions = [
    'matplotlib.sphinxext.plot_directive',
    'sphinx.ext.autodoc',
    'sphinx.ext.napoleon',
    'sphinx.ext.mathjax',
    'sphinx.ext.intersphinx',
    'breathe'
]

graphviz_output_format = 'png'
plot_formats = [('svg', 300), ('png', 100), ('hires.png', 300)]
plot_html_show_source_link = False
plot_html_show_formats = False

# Save an XGBoost model
    xgb_model.save_model(model_data_path)

    conda_env_subpath = "conda.yaml"
    if conda_env is None:
        conda_env = get_default_conda_env()
    elif not isinstance(conda_env, dict):
        with open(conda_env, "r") as f:
            conda_env = yaml.safe_load(f)
    with open(os.path.join(path, conda_env_subpath), "w") as f:
        yaml.safe_dump(conda_env, stream=f, default_flow_style=False)

    pyfunc.add_to_model(mlflow_model, loader_module="mlflow.xgboost",
                        data=model_data_subpath, env=conda_env_subpath)
    mlflow_model.add_flavor(FLAVOR_NAME, xgb_version=xgb.__version__, data=model_data_subpath)
    mlflow_model.save(os.path.join(path, "MLmodel"))

"""
    Checks that *xgboost* is available.
    """
    try:
        import xgboost  # noqa F401
    except ImportError:
        return False
    from xgboost.core import _LIB
    try:
        _LIB.XGBoosterDumpModelEx
    except AttributeError:
        # The version is not recent enough even though it is version 0.6.
        # You need to install xgboost from github and not from pypi.
        return False
    from xgboost import __version__
    vers = LooseVersion(__version__)
    allowed = LooseVersion('0.7')
    if vers < allowed:
        warnings.warn('The converter works for xgboost >= 0.7. Earlier versions might not.')
    return True

# check for unsupported combinations of feature_perturbation and model_outputs
        if feature_perturbation == "tree_path_dependent":
            if model_output != "margin":
                raise ValueError("Only margin model_output is supported for feature_perturbation=\"tree_path_dependent\"")
        elif data is None:
            raise ValueError("A background dataset must be provided unless you are using feature_perturbation=\"tree_path_dependent\"!")

        if model_output != "margin":
            if self.model.objective is None and self.model.tree_output is None:
                raise Exception("Model does not have a known objective or output type! When model_output is " \
                                "not \"margin\" then we need to know the model's objective or link function.")

        # A bug in XGBoost fixed in v0.81 makes XGBClassifier fail to give margin outputs
        if safe_isinstance(model, "xgboost.sklearn.XGBClassifier") and model_output != "margin":
            import xgboost
            if LooseVersion(xgboost.__version__) < LooseVersion('0.81'):
                raise RuntimeError("A bug in XGBoost fixed in v0.81 makes XGBClassifier fail to give margin outputs! Please upgrade to XGBoost >= v0.81!")

        # compute the expected value if we have a parsed tree for the cext
        if self.model_output == "logloss":
            self.expected_value = self.__dynamic_expected_value
        elif data is not None:
            try:
                self.expected_value = self.model.predict(self.data, output=model_output).mean(0)
            except:
                raise Exception("Currently TreeExplainer can only handle models with categorical splits when " \
                                "feature_perturbation=\"tree_path_dependent\" and no background data is passed. Please try again using " \
                                "shap.TreeExplainer(model, feature_perturbation=\"tree_path_dependent\").")
            if hasattr(self.expected_value, '__len__') and len(self.expected_value) == 1:
                self.expected_value = self.expected_value[0]
        elif hasattr(self.model, "node_sample_weight"):
            self.expected_value = self.model.values[:,0].sum(0)