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.
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docstr: Normalize between -1.0 to 1.0.
  
  Args:
      inputs: A Tensor of images we want to normalize. Its shape is
          (1, height, width, num_channels).
  Returns:
      outputs: A Tensor of images normalized between -1 and 1.
          Its shape is the same as the input.
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code: def _normalize(self, inputs):
      """Normalize between -1.0 to 1.0.
  
      Args:
          inputs: A Tensor of images we want to normalize. Its shape is
              (1, height, width, num_channels).
      Returns:
          outputs: A Tensor of images normalized between -1 and 1.
              Its shape is the same as the input.
      """
      inputs = inputs / 255.0
      inputs = (inputs - 0.5) * 2.0
      return inputs
  
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 15 for calling the BaseNetwork obj's underlying member method code with example usage: obj._normalize(inputs) and returns: inputs