class_docstr: Base class for weight constraints.
  
  A `Constraint` instance works like a stateless function.
  Users who subclass this
  class should override the `__call__()` method, which takes a single
  weight parameter and return a projected version of that parameter
  (e.g. normalized or clipped). Constraints can be used with various Keras
  layers via the `kernel_constraint` or `bias_constraint` arguments.
  
  Here's a simple example of a non-negative weight constraint:
  
  >>> class NonNegative(keras.constraints.Constraint):
  ...
  ...  def __call__(self, w):
  ...    return w * ops.cast(ops.greater_equal(w, 0.), dtype=w.dtype)
  
  >>> weight = ops.convert_to_tensor((-1.0, 1.0))
  >>> NonNegative()(weight)
  [0.,  1.]
  
  Usage in a layer:
  
  >>> keras.layers.Dense(4, kernel_constraint=NonNegative())
docstr: Returns a Python dict of the object config.
  
  A constraint config is a Python dictionary (JSON-serializable) that can
  be used to reinstantiate the same object.
  
  Returns:
      Python dict containing the configuration of the constraint object.
docstr_tok: ["Returns","a","Python","dict","of","the","object","config",".","A","constraint","config","is","a","Python","dictionary","(","JSON-serializable",")","that","can","be","used","to","reinstantiate","the","same","object",".","Returns",":","Python","dict","containing","the","configuration","of","the","constraint","object","."]
code: def get_config(self):
      """Returns a Python dict of the object config.
  
      A constraint config is a Python dictionary (JSON-serializable) that can
      be used to reinstantiate the same object.
  
      Returns:
          Python dict containing the configuration of the constraint object.
      """
      return {}
  
imports: from keras import backend
  from keras import ops
  from keras.api_export import keras_export
label_desc: Use image node_id 2 for calling the Constraint obj's underlying member method code with example usage: obj.get_config() and returns: dict