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","."]
code: def __init__(self, config, name="base_network"):
      super(BaseNetwork, self).__init__(name=name)
      if config.get("architecture") not in VALID_ARCHITECTURES:
          raise ValueError(
              'Invalid architecture: "{}"'.format(
                  config.get("architecture")
              )
          )
  
      self._architecture = config.get("architecture")
      self._config = config
  
      self.pretrained_weights_scope = None
  
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 1 to create a new BaseNetwork object from inherited base classes: snt with example: obj = BaseNetwork(config, name)