text: import tensorflow as tf
  
  def unet(pretrained_weights = None, input_size = (1024,1024,1)):
      inputs = tf.keras.layers.Input(input_size)
      conv1 = tf.keras.layers.Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
      conv1 = tf.keras.layers.Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
      pool1 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(conv1)
      conv2 = tf.keras.layers.Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
      conv2 = tf.keras.layers.Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
      pool2 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(conv2)
      conv3 = tf.keras.layers.Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
      conv3 = tf.keras.layers.Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
      pool3 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(conv3)
      conv4 = tf.keras.layers.Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
      conv4 = tf.keras.layers.Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
      drop4 = tf.keras.layers.Dropout(0.5)(conv4)
      pool4 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(drop4)
  
      conv5 = tf.keras.layers.Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
      conv5 = tf.keras.layers.Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
      drop5 = tf.keras.layers.Dropout(0.5)(conv5)
  
      up6 = tf.keras.layers.Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(tf.keras.layers.UpSampling2D(size = (2,2))(drop5))
      merge6 = tf.keras.layers.concatenate([drop4,up6], axis = 3)
      conv6 = tf.keras.layers.Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
      conv6 = tf.keras.layers.Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
  
      up7 = tf.keras.layers.Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(tf.keras.layers.UpSampling2D(size = (2,2))(conv6))
      merge7 = tf.keras.layers.concatenate([conv3,up7], axis = 3)
      conv7 = tf.keras.layers.Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
      conv7 = tf.keras.layers.Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)
  
      up8 = tf.keras.layers.Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(tf.keras.layers.UpSampling2D(size = (2,2))(conv7))
      merge8 = tf.keras.layers.concatenate([conv2,up8], axis = 3)
      conv8 = tf.keras.layers.Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
      conv8 = tf.keras.layers.Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)
  
      up9 = tf.keras.layers.Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(tf.keras.layers.UpSampling2D(size = (2,2))(conv8))
      merge9 = tf.keras.layers.concatenate([conv1,up9], axis = 3)
      conv9 = tf.keras.layers.Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
      conv9 = tf.keras.layers.Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
      conv9 = tf.keras.layers.Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
      conv10 = tf.keras.layers.Conv2D(1, 1, activation = 'sigmoid')(conv9)
  
      return tf.keras.Model(inputs = [inputs], outputs = [conv10])