class_docstr: Convolutional Neural Network used for image classification, whose
  architecture can be any of the `VALID_ARCHITECTURES`.
  
  This class wraps the `tf.slim` implementations of these models, with some
  helpful additions.
class_docstr_tok: ["Convolutional","Neural","Network","used","for","image","classification",",","whose","architecture","can","be","any","of","the","`","VALID_ARCHITECTURES","`",".","This","class","wraps","the","`","tf.slim","`","implementations","of","these","models",",","with","some","helpful","additions","."]
docstr: Returns a list of all the base network's variables.
docstr_tok: ["Returns","a","list","of","all","the","base","network","'s","variables","."]
code: def _get_base_network_vars(self):
      """Returns a list of all the base network's variables."""
      if self.pretrained_weights_scope:
          # We may have defined the base network in a particular scope
          module_variables = tf.get_collection(
              tf.GraphKeys.MODEL_VARIABLES,
              scope=self.pretrained_weights_scope,
          )
      else:
          module_variables = snt.get_variables_in_module(
              self, tf.GraphKeys.MODEL_VARIABLES
          )
      assert len(module_variables) > 0
      return module_variables
  
imports: import functools
  import sonnet
  import tensorflow
  import tensorflow.contrib.slim
  from tensorflow.contrib.slim.nets import resnet_v2, resnet_v1, vgg
  from luminoth.models.base import truncated_vgg
  from luminoth.utils.checkpoint_downloader import get_checkpoint_file
label_desc: Use image node_id 17 for calling the BaseNetwork obj's underlying member method code with example usage: obj._get_base_network_vars() and returns: module_variables