class_docstr: A simple model that uses only convolution and downsampling---no batch norm or other techniques that can complicate
  adversarial training.
code: def __init__(
      self, scope, nb_classes, nb_filters, input_shape, **kwargs
  ):
      del kwargs
      NoRefModel.__init__(self, scope, nb_classes, locals())
      self.nb_filters = nb_filters
      self.input_shape = input_shape
  
      # Do a dummy run of fprop to create the variables from the start
      self.fprop(tf.placeholder(tf.float32, [32] + input_shape))
      # Put a reference to the params in self so that the params get pickled
      self.params = self.get_params()
  
file_path: cleverhans/cleverhans_v3.1.0/cleverhans/model_zoo/all_convolutional.py
imports: import math
  import tensorflow
  from cleverhans import initializers
  from cleverhans.serial import NoRefModel
label_desc: Use image node_id 1 to create a new ModelAllConvolutional object from inherited base classes: NoRefModel with example: obj = ModelAllConvolutional(scope, nb_classes, nb_filters, input_shape)