model_type = cftrain.model_parameters['model_type']
    output_type = cftrain.model_parameters['output_type']
    batch_size = cftrain.model_parameters['batch_size']
    epocs = cftrain.model_parameters['epocs']
    split_ratio = cftrain.model_parameters['split_ratio']
    optimizer = cftrain.model_parameters['optimizer']
    loss_func = cftrain.model_parameters['loss_func']
    regularizer_coeff = cftrain.model_parameters['regularizer_coeff']
    activate_tensorboard = cftrain.model_parameters['activate_tensorboard']

    print('Initializing the Assembly System and Measurement System....')
    measurement_system = HexagonWlsScanner(data_type, application,
                                           system_noise, part_type,
                                           data_format)
    vrm_system = VRMSimulationModel(assembly_type, assembly_kccs,
                                    assembly_kpis, part_name, part_type,
                                    voxel_dim, voxel_channels, point_dim,
                                    aritifical_noise)
    deploy_model = BayesDeployModel()

    #Generate Paths
    train_path = '../trained_models/' + part_type
    model_path = train_path + '/model' + '/Bayes_trained_model_0'
    logs_path = train_path + '/logs'
    deploy_path = train_path + '/deploy/'
    plots_path = train_path + '/plots/'

    #Voxel Mapping File

    get_data = GetTrainData()

    print('Importing and Preprocessing Cloud-of-Point Data')
    pathlib.Path(logs_path).mkdir(parents=True, exist_ok=True)

    plots_path = train_path + '/plots'
    pathlib.Path(plots_path).mkdir(parents=True, exist_ok=True)

    deployment_path = train_path + '/deploy'
    pathlib.Path(deployment_path).mkdir(parents=True, exist_ok=True)

    #Objects of Measurement System, Assembly System, Get Inference Data
    print('Initializing the Assembly System and Measurement System....')

    measurement_system = HexagonWlsScanner(data_type, application,
                                           system_noise, part_type,
                                           data_format)
    vrm_system = VRMSimulationModel(assembly_type, assembly_kccs,
                                    assembly_kpis, part_name, part_type,
                                    voxel_dim, voxel_channels, point_dim,
                                    aritifical_noise)
    get_data = GetTrainData()

    kcc_sublist = cftrain.encode_decode_params['kcc_sublist']

    #Check for KCC sub-listing
    if (kcc_sublist != 0):
        output_dimension = len(kcc_sublist)
    else:
        output_dimension = assembly_kccs

    #print(input_conv_data.shape,kcc_subset_dump.shape)
    print('Building Unet Model')

    output_dimension = assembly_kccs