How to use the dowhy.datasets.linear_dataset function in dowhy

def test_pandas_api_continuous_cause_continuous_confounder(self, N, error_tolerance):
        data = dowhy.datasets.linear_dataset(beta=10,
                                             num_common_causes=1,
                                             num_instruments=1,
                                             num_samples=N,
                                             treatment_is_binary=False)
        X0 = np.random.normal(size=N)
        v = np.random.normal(size=N) + X0
        y = data['ate'] * v + X0 + np.random.normal()
        data['df']['v'] = v
        data['df']['X0'] = X0
        data['df']['y'] = y
        df = data['df'].copy()

        variable_types = {'v': 'c', 'X0': 'c', 'y': 'c'}
        outcome = 'y'
        cause = 'v'
        common_causes = 'X0'

def test_pandas_api_discrete_cause_discrete_confounder(self, N, error_tolerance):
        data = dowhy.datasets.linear_dataset(beta=10,
                                             num_common_causes=1,
                                             num_instruments=1,
                                             num_samples=N,
                                             treatment_is_binary=False)
        X0 = np.random.normal(size=N).astype(int)
        v = (np.random.normal(size=N) + X0).astype(int)
        y = data['ate'] * v + X0 + np.random.normal()
        data['df']['v'] = v
        data['df']['X0'] = X0
        data['df']['y'] = y
        df = data['df'].copy()

        variable_types = {'v': 'd', 'X0': 'd', 'y': 'c'}
        outcome = 'y'
        cause = 'v'
        common_causes = 'X0'