class_docstr: An abstract interface for model wrappers that exposes model symbols
  needed for making an attack. This abstraction removes the dependency on
  any specific neural network package (e.g. Keras) from the core
  code of CleverHans. It can also simplify exposing the hidden features of a
  model when a specific package does not directly expose them.
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docstr: Create all Variables to be returned later by get_params.
  By default this is a no-op.
  Models that need their fprop to be called for their params to be
  created can set `needs_dummy_fprop=True` in the constructor.
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code: def make_params(self):
      """
      Create all Variables to be returned later by get_params.
      By default this is a no-op.
      Models that need their fprop to be called for their params to be
      created can set `needs_dummy_fprop=True` in the constructor.
      """
  
      if self.needs_dummy_fprop:
          if hasattr(self, "_dummy_input"):
              return
          self._dummy_input = self.make_input_placeholder()
          self.fprop(self._dummy_input)
  
imports: from abc import ABCMeta
  import warnings
  import tensorflow
  from cleverhans import utils_tf
label_desc: Use image node_id 8 for calling the Model obj's underlying member method code with example usage: obj.make_params() without return types