code: def network_a(batch_input_shape, classes, private=True):
      if private:
          model = tfe.keras.Sequential()
          model.add(
              tfe.keras.layers.Flatten(
                  batch_input_shape=batch_input_shape
              )
          )
          model.add(
              tfe.keras.layers.Dense(
                  128, activation=None, lazy_normalization=True
              )
          )
          model.add(tfe.keras.layers.ReLU())
          model.add(
              tfe.keras.layers.Dense(
                  128, activation=None, lazy_normalization=True
              )
          )
          model.add(tfe.keras.layers.ReLU())
          model.add(
              tfe.keras.layers.Dense(
                  classes, activation=None, lazy_normalization=True
              )
          )
          return model
      else:
          model = tf.keras.Sequential()
          model.add(
              tf.keras.layers.Flatten(
                  batch_input_shape=batch_input_shape
              )
          )
          model.add(tf.keras.layers.Dense(128, activation=None))
          model.add(tf.keras.layers.ReLU())
          model.add(tf.keras.layers.Dense(128, activation=None))
          model.add(tf.keras.layers.ReLU())
          model.add(tf.keras.layers.Dense(classes, activation=None))
          return model
  
label_desc: Use image node_id 1 for calling a global function with example usage: network_a(batch_input_shape, classes, private) and returns: model, model