text: # You can call the needed layers only
  # Please call the packages that are required to define the network architecture
  from keras.models import Model
  import tensorflow as tf
  from keras.layers import * 
  from keras import backend as K
  
  # This is the master class of the network, in case you build may build many networks
  class DLNetwork(object):
      # This is the abstract init function
      def __init__(self, image_w, image_h, channel):
          raise NotImplementedError('You have to implement it in the derived class!')
  
      # This is the normalize function for all class
      def normImage(self, image):
          return (image / 127.5) - 1
  
  
  # The newNetwork class is derived from the master class 'DLNetwork', it will share the normImage function as its function too
  class newNetwork(DLNetwork):
      # This is the init function for newNetwork class
      def __init__(self, image_w, image_h, channel):
  
          # This is the input in the network
          input = Input(shape=(image_h,image_w,3))
  
          # Implement your network architecture here!
          x = Conv2D(3,kernel_size=3,padding='same',kernel_initializer='he_normal')(input)
          x = LeakyReLU(alpha=0.1)(x)
          x = Conv2D(3,kernel_size=3,padding='same',kernel_initializer='he_normal')(x)
          x = LeakyReLU(alpha=0.1)(x)
          x = Conv2D(3,kernel_size=3,padding='same',kernel_initializer='he_normal')(x)
          x = LeakyReLU(alpha=0.1)(x)
          x = Conv2D(3,kernel_size=3,padding='same',kernel_initializer='he_normal')(x)
          x = LeakyReLU(alpha=0.1)(x)
          x = Conv2D(3,kernel_size=3,padding='same',kernel_initializer='he_normal')(x)
          x = LeakyReLU(alpha=0.1)(x)
  
          # This is the output in the network
          output = Conv2D(3,kernel_size=3,padding='same',kernel_initializer='he_normal')(x)
  
          # The model is generated based on the input and output, it is saved in self.model
          self.model = Model(input,output)