How to use the abcpy.output.Journal.fromFile function in abcpy

print(journal.parameters)
    print(journal.weights)
    
    # do post analysis
    print(journal.posterior_mean())
    print(journal.posterior_cov())
    print(journal.posterior_histogram())
    
    # print configuration
    print(journal.configuration)
    
    # save and load journal
    journal.save("experiments.jnl")

    from abcpy.output import Journal
    new_journal = Journal.fromFile('experiments.jnl')

journal = Journal(full_output)
            journal.configuration["type_model"] = [type(model).__name__ for model in self.model]
            journal.configuration["type_dist_func"] = type(self.distance).__name__
            journal.configuration["type_kernel_func"] = type(self.kernel)
            journal.configuration["n_samples"] = self.n_samples
            journal.configuration["n_samples_per_param"] = self.n_samples_per_param
            journal.configuration["beta"] = beta
            journal.configuration["delta"] = delta
            journal.configuration["v"] = v
            journal.configuration["ar_cutoff"] = ar_cutoff
            journal.configuration["resample"] = resample
            journal.configuration["n_update"] = n_update
            journal.configuration["adaptcov"] = adaptcov
            journal.configuration["full_output"] = full_output
        else:
            journal = Journal.fromFile(journal_file)

        accepted_parameters = np.zeros(shape=(n_samples, len(self.get_parameters(self.model))))
        distances = np.zeros(shape=(n_samples,))
        smooth_distances = np.zeros(shape=(n_samples,))
        accepted_weights = np.ones(shape=(n_samples, 1))
        all_distances = None
        accepted_cov_mat = None

        if resample == None:
            resample = n_samples
        if n_update == None:
            n_update = n_samples
        sample_array = np.ones(shape=(steps,))
        sample_array[0] = n_samples
        sample_array[1:] = n_update

journal.get_parameters()
    journal.get_weights()
    
    # do post analysis
    journal.posterior_mean()
    journal.posterior_cov()
    journal.posterior_histogram()
    
    # print configuration
    print(journal.configuration)
    
    # save and load journal
    journal.save("experiments.jnl")
    
    from abcpy.output import Journal
    new_journal = Journal.fromFile('experiments.jnl')

    journal.plot_posterior_distr()

print(journal.parameters)
    print(journal.weights)

    # do post analysis
    print(journal.posterior_mean())
    print(journal.posterior_cov())
    print(journal.posterior_histogram())
    
    # print configuration
    print(journal.configuration)

    # save and load journal
    journal.save("experiments.jnl")
    
    from abcpy.output import Journal
    new_journal = Journal.fromFile('experiments.jnl')

print(journal.get_stored_output_values())
    print(journal.weights)

    # do post analysis
    print(journal.posterior_mean())
    print(journal.posterior_cov())
    print(journal.posterior_histogram())

    # print configuration
    print(journal.configuration)

    # save and load journal
    journal.save("experiments.jnl")

    from abcpy.output import Journal
    new_journal = Journal.fromFile('experiments.jnl')

print(journal.get_stored_output_values())
    print(journal.weights)

    # do post analysis
    print(journal.posterior_mean())
    print(journal.posterior_cov())
    print(journal.posterior_histogram())

    # print configuration
    print(journal.configuration)

    # save and load journal
    journal.save("experiments.jnl")

    from abcpy.output import Journal
    new_journal = Journal.fromFile('experiments.jnl')