class_docstr: Extends the Layer object to have trainable parameters,
  adding initializers and regularizers.
code: def trainable_variables(self):
      return tf.get_collection(
          tf.GraphKeys.TRAINABLE_VARIABLES, self.layer_scope().name
      )
  
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 2 for calling the TrainableLayer obj's underlying member method code with example usage: obj.trainable_variables() and returns: tf