How to use the miscnn.neural_network.architecture.abstract_architecture.Abstract_Architecture function in miscnn

from tensorflow.keras.layers import BatchNormalization
# Internal libraries/scripts
from miscnn.neural_network.architecture.abstract_architecture import Abstract_Architecture

#-----------------------------------------------------#
#         Architecture class: U-Net Residual          #
#-----------------------------------------------------#
""" The Residual variant of the popular U-Net architecture.
    It uses additional concatenate layers after each convolutional block (2x conv layers).

Methods:
    __init__                Object creation function
    create_model_2D:        Creating the 2D U-Net Residual model using Keras
    create_model_3D:        Creating the 3D U-Net Residual model using Keras
"""
class Architecture(Abstract_Architecture):
    #---------------------------------------------#
    #                Initialization               #
    #---------------------------------------------#
    def __init__(self, n_filters=32, depth=4, activation='sigmoid',
                 batch_normalization=True):
        # Parse parameter
        self.n_filters = n_filters
        self.depth = depth
        self.activation = activation
        # Batch normalization settings
        self.ba_norm = batch_normalization
        self.ba_norm_momentum = 0.99

    #---------------------------------------------#
    #               Create 2D Model               #
    #---------------------------------------------#

from tensorflow.keras.layers import ELU, LeakyReLU
# Internal libraries/scripts
from miscnn.neural_network.architecture.abstract_architecture import Abstract_Architecture


#-----------------------------------------------------#
#         Architecture class: U-Net MultiRes          #
#-----------------------------------------------------#
""" The MultiRes variant of the popular U-Net architecture.

Methods:
    __init__                Object creation function
    create_model_2D:        Creating the 2D U-Net standard model using Keras
    create_model_3D:        Creating the 3D U-Net standard model using Keras
"""
class Architecture(Abstract_Architecture):
    #---------------------------------------------#
    #                Initialization               #
    #---------------------------------------------#
    def __init__(self, activation='sigmoid'):
        # Parse parameter
        self.activation = activation

    #---------------------------------------------#
    #               Create 2D Model               #
    #---------------------------------------------#
    def create_model_2D(self, input_shape, n_labels=2):
        # Input layer
        inputs = Input(input_shape)

        mresblock1 = MultiResBlock_2D(32, inputs)
        pool1 = MaxPooling2D(pool_size=(2, 2))(mresblock1)

from tensorflow.keras.layers import BatchNormalization
# Internal libraries/scripts
from miscnn.neural_network.architecture.abstract_architecture import Abstract_Architecture

#-----------------------------------------------------#
#          Architecture class: U-Net Compact          #
#-----------------------------------------------------#
""" The Compact variant of the popular U-Net architecture.
    It uses additional concatenate layers after each convolutional block (2x conv layers).

Methods:
    __init__                Object creation function
    create_model_2D:        Creating the 2D U-Net Compact model using Keras
    create_model_3D:        Creating the 3D U-Net Compact model using Keras
"""
class Architecture(Abstract_Architecture):
    #---------------------------------------------#
    #                Initialization               #
    #---------------------------------------------#
    def __init__(self, n_filters=32, depth=4, activation='sigmoid',
                 batch_normalization=True):
        # Parse parameter
        self.n_filters = n_filters
        self.depth = depth
        self.activation = activation
        # Batch normalization settings
        self.ba_norm = batch_normalization
        self.ba_norm_momentum = 0.99

    #---------------------------------------------#
    #               Create 2D Model               #
    #---------------------------------------------#

from tensorflow.keras.layers import Conv2D, MaxPooling2D, Conv2DTranspose
from tensorflow.keras.layers import BatchNormalization
# Internal libraries/scripts
from miscnn.neural_network.architecture.abstract_architecture import Abstract_Architecture

#-----------------------------------------------------#
#         Architecture class: U-Net Standard          #
#-----------------------------------------------------#
""" The Standard variant of the popular U-Net architecture.

Methods:
    __init__                Object creation function
    create_model_2D:        Creating the 2D U-Net standard model using Keras
    create_model_3D:        Creating the 3D U-Net standard model using Keras
"""
class Architecture(Abstract_Architecture):
    #---------------------------------------------#
    #                Initialization               #
    #---------------------------------------------#
    def __init__(self, n_filters=32, depth=4, activation='softmax',
                 batch_normalization=True):
        # Parse parameter
        self.n_filters = n_filters
        self.depth = depth
        self.activation = activation
        # Batch normalization settings
        self.ba_norm = batch_normalization
        self.ba_norm_momentum = 0.99

    #---------------------------------------------#
    #               Create 2D Model               #
    #---------------------------------------------#

from tensorflow.keras.layers import Conv2D, MaxPooling2D, Conv2DTranspose
from tensorflow.keras.layers import BatchNormalization
# Internal libraries/scripts
from miscnn.neural_network.architecture.abstract_architecture import Abstract_Architecture

#-----------------------------------------------------#
#           Architecture class: U-Net Plain           #
#-----------------------------------------------------#
""" The Plain variant of the popular U-Net architecture.

Methods:
    __init__                Object creation function
    create_model_2D:        Creating the 2D U-Net plain model using Keras
    create_model_3D:        Creating the 3D U-Net plain model using Keras
"""
class Architecture(Abstract_Architecture):
    #---------------------------------------------#
    #                Initialization               #
    #---------------------------------------------#
    def __init__(self, activation='softmax', batch_normalization=True):
        # Parse parameter
        self.activation = activation
        # Batch normalization settings
        self.ba_norm = batch_normalization
        # Create list of filters
        self.feature_map = [30, 60, 120, 240, 320]

    #---------------------------------------------#
    #               Create 2D Model               #
    #---------------------------------------------#
    def create_model_2D(self, input_shape, n_labels=2):
        # Input layer

from tensorflow.keras.layers import BatchNormalization
# Internal libraries/scripts
from miscnn.neural_network.architecture.abstract_architecture import Abstract_Architecture

#-----------------------------------------------------#
#           Architecture class: U-Net Dense           #
#-----------------------------------------------------#
""" The Dense variant of the popular U-Net architecture.
    It uses additional concatenate layers after each convolutional layer.

Methods:
    __init__                Object creation function
    create_model_2D:        Creating the 2D U-Net Dense model using Keras
    create_model_3D:        Creating the 3D U-Net Dense model using Keras
"""
class Architecture(Abstract_Architecture):
    #---------------------------------------------#
    #                Initialization               #
    #---------------------------------------------#
    def __init__(self, n_filters=32, depth=4, activation='sigmoid',
                 batch_normalization=True):
        # Parse parameter
        self.n_filters = n_filters
        self.depth = depth
        self.activation = activation
        # Batch normalization settings
        self.ba_norm = batch_normalization
        self.ba_norm_momentum = 0.99

    #---------------------------------------------#
    #               Create 2D Model               #
    #---------------------------------------------#