How to use the abcpy.distances.Euclidean function in abcpy

# define prior
    from abcpy.continuousmodels import Uniform
    mu = Uniform([[150], [200]], name='mu')
    sigma = Uniform([[5], [25]], name='sigma')

    # define the model
    from abcpy.continuousmodels import Normal
    height = Normal([mu, sigma], name='height')

    # define statistics
    from abcpy.statistics import Identity
    statistics_calculator = Identity(degree = 2, cross = False)

    # define distance
    from abcpy.distances import Euclidean
    distance_calculator = Euclidean(statistics_calculator)

    # define sampling scheme
    from abcpy.inferences import PMCABC
    sampler = PMCABC([height], [distance_calculator], backend, seed=1)
    
    # sample from scheme
    T, n_sample, n_samples_per_param = 2, 10, 1
    eps_arr = np.array([10000])
    epsilon_percentile = 95
    journal = sampler.sample([y_obs],  T, eps_arr, n_sample, n_samples_per_param, epsilon_percentile)

    return journal

# Redefine the statistics function
    new_statistics_calculator = statistics_learning.get_statistics()


    # Learn the optimal summary statistics using SemiautomaticNN summary selection
    from abcpy.statisticslearning import SemiautomaticNN
    statistics_learning = SemiautomaticNN([height], statistics_calculator, backend,
                                        n_samples=1000,n_samples_per_param=1, seed=1)

    # Redefine the statistics function
    new_statistics_calculator = statistics_learning.get_statistics()


    # define distance
    from abcpy.distances import Euclidean
    distance_calculator = Euclidean(new_statistics_calculator)

    # define kernel
    from abcpy.perturbationkernel import DefaultKernel
    kernel = DefaultKernel([mu, sigma])

    # define sampling scheme
    from abcpy.inferences import PMCABC
    sampler = PMCABC([height], [distance_calculator], backend, kernel, seed=1)

    # sample from scheme
    T, n_sample, n_samples_per_param = 3, 10, 10
    eps_arr = np.array([500])
    epsilon_percentile = 10
    journal = sampler.sample([height_obs],  T, eps_arr, n_sample, n_samples_per_param, epsilon_percentile)

    return journal

# define prior
    from abcpy.continuousmodels import Uniform
    mu0 = Uniform([[150], [200]], )
    mu1 = Uniform([[25], [100]], )

    # define the model
    height_weight_model = NestedBivariateGaussian([mu0, mu1])

    # define statistics
    from abcpy.statistics import Identity
    statistics_calculator = Identity(degree = 2, cross = False)

    # define distance
    from abcpy.distances import Euclidean
    distance_calculator = Euclidean(statistics_calculator)

    # define sampling scheme
    from abcpy.inferences import RSMCABC
    sampler = RSMCABC([height_weight_model], [distance_calculator], backend, seed=1)
    print('sampling')
    steps, n_samples, n_samples_per_param, alpha, epsilon_init, epsilon_final = 2, 10, 1, 0.1, 10000, 500
    print('RSMCABC Inferring')
    journal = sampler.sample([y_obs], steps, n_samples, n_samples_per_param, alpha , epsilon_init, epsilon_final,full_output=1)

    return journal

# define prior
    from abcpy.continuousmodels import Uniform
    mu0 = Uniform([[150], [200]], )
    mu1 = Uniform([[25], [100]], )

    # define the model
    height_weight_model = NestedBivariateGaussian([mu0, mu1])

    # define statistics
    from abcpy.statistics import Identity
    statistics_calculator = Identity(degree = 2, cross = False)

    # define distance
    from abcpy.distances import Euclidean
    distance_calculator = Euclidean(statistics_calculator)

    # define sampling scheme
    from abcpy.inferences import PMCABC
    sampler = PMCABC([height_weight_model], [distance_calculator], backend, seed=1)
    # sample from scheme
    T, n_sample, n_samples_per_param = 2, 10, 1
    eps_arr = np.array([10000])
    epsilon_percentile = 95

    journal = sampler.sample([y_obs],  T, eps_arr, n_sample, n_samples_per_param, epsilon_percentile)

    return journal

scholarship_obs = [2.7179657436207805, 2.124647285937229, 3.07193407853297, 2.335024761813643, 2.871893855192, 3.4332002458233837, 3.649996835818173, 3.50292335102711, 2.815638168018455, 2.3581613289315992, 2.2794821846395568, 2.8725835459926503, 3.5588573782815685, 2.26053126526137, 1.8998143530749971, 2.101110815311782, 2.3482974964831573, 2.2707679029919206, 2.4624550491079225, 2.867017757972507, 3.204249152084959, 2.4489542437714213, 1.875415915801106, 2.5604889644872433, 3.891985093269989, 2.7233633223405205, 2.2861070389383533, 2.9758813233490082, 3.1183403287267755, 2.911814060853062, 2.60896794303205, 3.5717098647480316, 3.3355752461779824, 1.99172284546858, 2.339937680892163, 2.9835630207301636, 2.1684912355975774, 3.014847335983034, 2.7844122961916202, 2.752119871525148, 2.1567428931391635, 2.5803629307680644, 2.7326646074552103, 2.559237193255186, 3.13478196958166, 2.388760269933492, 3.2822443541491815, 2.0114405441787437, 3.0380056368041073, 2.4889680313769724, 2.821660164621084, 3.343985964873723, 3.1866861970287808, 4.4535037154856045, 3.0026333138006027, 2.0675706089352612, 2.3835301730913185, 2.584208398359566, 3.288077633446465, 2.6955853384148183, 2.918315169739928, 3.2464814419322985, 2.1601516779909433, 3.231003347780546, 1.0893224045062178, 0.8032302688764734, 2.868438615047827]

    # A quantity that determines whether a student will receive a scholarship
    scholarship_without_additional_effects = Normal([[2], [0.5]], )

    # A quantity determining whether a student receives a scholarship, including his social background
    final_scholarship = scholarship_without_additional_effects + 3*background

    # Define a summary statistics for final grade and final scholarship
    from abcpy.statistics import Identity
    statistics_calculator_final_grade = Identity(degree = 2, cross = False)
    statistics_calculator_final_scholarship = Identity(degree = 3, cross = False)

    # Define a distance measure for final grade and final scholarship
    from abcpy.distances import Euclidean
    distance_calculator_final_grade = Euclidean(statistics_calculator_final_grade)
    distance_calculator_final_scholarship = Euclidean(statistics_calculator_final_scholarship)

    # Define a backend
    from abcpy.backends import BackendDummy as Backend
    backend = Backend()

    # Define a perturbation kernel
    from abcpy.perturbationkernel import DefaultKernel
    kernel = DefaultKernel([school_location, class_size, grade_without_additional_effects, \
                            background, scholarship_without_additional_effects])

    # Define sampling parameters
    T, n_sample, n_samples_per_param = 3, 250, 10
    eps_arr = np.array([.75])
    epsilon_percentile = 10

# define prior
    from abcpy.continuousmodels import Uniform
    mu0 = Uniform([[150], [200]], )
    mu1 = Uniform([[25], [100]], )

    # define the model
    height_weight_model = NestedBivariateGaussian([mu0, mu1])

    # define statistics
    from abcpy.statistics import Identity
    statistics_calculator = Identity(degree = 2, cross = False)

    # define distance
    from abcpy.distances import Euclidean
    distance_calculator = Euclidean(statistics_calculator)

    # define sampling scheme
    from abcpy.inferences import RejectionABC
    sampler = RejectionABC([height_weight_model], [distance_calculator], backend, seed=1)
    n_samples, n_samples_per_param, epsilon = 2, 1, 20000
    journal = sampler.sample([y_obs], n_samples, n_samples_per_param, epsilon)

    return journal

# A quantity that determines whether a student will receive a scholarship
    scholarship_without_additional_effects = Normal([[2], [0.5]], )

    # A quantity determining whether a student receives a scholarship, including his social background
    final_scholarship = scholarship_without_additional_effects + 3*background

    # Define a summary statistics for final grade and final scholarship
    from abcpy.statistics import Identity
    statistics_calculator_final_grade = Identity(degree = 2, cross = False)
    statistics_calculator_final_scholarship = Identity(degree = 3, cross = False)

    # Define a distance measure for final grade and final scholarship
    from abcpy.distances import Euclidean
    distance_calculator_final_grade = Euclidean(statistics_calculator_final_grade)
    distance_calculator_final_scholarship = Euclidean(statistics_calculator_final_scholarship)

    # Define a backend
    from abcpy.backends import BackendDummy as Backend
    backend = Backend()

    # Define a perturbation kernel
    from abcpy.perturbationkernel import DefaultKernel
    kernel = DefaultKernel([school_location, class_size, grade_without_additional_effects, \
                            background, scholarship_without_additional_effects])

    # Define sampling parameters
    T, n_sample, n_samples_per_param = 3, 250, 10
    eps_arr = np.array([.75])
    epsilon_percentile = 10

    # Define sampler