class_docstr: Extends the Layer object to have trainable parameters,
  adding initializers and regularizers.
code: def restore_from_checkpoint(self, checkpoint_name, scope=None):
      if scope is None:
          scope = self.layer_scope().name
      tf.add_to_collection(
          RESTORABLE, (self.layer_scope().name, checkpoint_name, scope)
      )
  
imports: from __future__ import absolute_import, print_function
  from abc import ABCMeta, abstractmethod
  import numpy
  import tensorflow
  from six import with_metaclass
  from niftynet.engine.application_variables import RESTORABLE
label_desc: Use image node_id 3 for calling the TrainableLayer obj's underlying member method code with example usage: obj.restore_from_checkpoint(checkpoint_name, scope) without return types