text: import tensorflow as tf
  
  from models.maml.maml import ModelAgnosticMetaLearningModel
  from networks.maml_umtra_networks import get_transfer_net
  
  
  class TransferLearningVGG16(ModelAgnosticMetaLearningModel):
      def __init__(
              self,
              database,
              n,
              k_val_test,
              k_test,
              lr_inner_ml,
              number_of_tasks_test,
              val_test_batch_norm_momentum,
              random_layer_initialization_seed,
              num_trainable_layers,
      ):
          self.random_layer_initialization_seed = random_layer_initialization_seed
          self.num_trainable_layers = num_trainable_layers
  
          super(TransferLearningVGG16, self).__init__(
              database=database,
              network_cls=None,
              n=n,
              k=-1,
              k_val_ml=-1,
              k_val_val=-1,
              k_val_test=k_val_test,
              k_test=k_test,
              meta_batch_size=-1,
              num_steps_ml=-1,
              lr_inner_ml=lr_inner_ml,
              num_steps_validation=-1,
              save_after_iterations=-1,
              meta_learning_rate=-1,
              report_validation_frequency=-1,
              log_train_images_after_iteration=-1,  # Set to -1 if you do not want to log train images.
              number_of_tasks_val=-1,  # Make sure the validation pick this many tasks.
              number_of_tasks_test=number_of_tasks_test,  # Make sure the validation pick this many tasks.
              val_seed=-1,  # The seed for validation dataset. -1 means change the samples for each report.
              clip_gradients=False,
              experiment_name=None,
              val_test_batch_norm_momentum=val_test_batch_norm_momentum,
          )
          # self.inner_opt = tf.keras.optimizers.SGD(self.lr_inner_ml, momentum=0.9)
          # self.inner_opt = tf.keras.optimizers.Adam(self.lr_inner_ml)
  
      def get_network_name(self):
          return 'VGG16'
  
      @tf.function
      def _train_model_for_eval(self, train_ds, train_labels):
          with tf.GradientTape(persistent=False) as train_tape:
              train_tape.watch(self.eval_model.meta_trainable_variables)
              logits = self.eval_model(train_ds, training=True)
              loss = self.inner_loss(train_labels, logits)
              tf.print(loss)
          # gradients = self.inner_opt.get_gradients(loss, self.eval_model.meta_trainable_variables)
          # self.inner_opt.apply_gradients(zip(gradients, self.eval_model.meta_trainable_variables))
          gradients = train_tape.gradient(loss, self.eval_model.meta_trainable_variables)
          self.create_meta_model(self.eval_model, self.eval_model, gradients, assign=True)
  
      def initialize_network(self):
          model = get_transfer_net(
              architecture='VGG16',
              num_trainable_layers=self.num_trainable_layers,
              num_classes=self.n,
              random_layer_initialization_seed=self.random_layer_initialization_seed
          )
          model(tf.zeros(shape=(1, 224, 224, 3)))
          return model
  
      def get_parse_function(self):
          def parse_function(example_address):
              image = tf.image.decode_jpeg(tf.io.read_file(example_address))
              image = tf.cast(image, tf.float32)
              image = tf.image.resize(image, (224, 224))
              image = tf.keras.applications.vgg16.preprocess_input(image)
              return image
  
          return parse_function
  