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(tf.keras.constraints.Constraint):
  ...
  ...  def __call__(self, w):
  ...    return w * tf.cast(tf.math.greater_equal(w, 0.), w.dtype)
  
  >>> weight = tf.constant((-1.0, 1.0))
  >>> NonNegative()(weight)
  <tf.Tensor: shape=(2,), dtype=float32, numpy=array([0.,  1.], dtype=float32)>
  
  >>> tf.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 tensorflow.python.framework import tensor_shape
  from tensorflow.python.keras import backend
  from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object
  from tensorflow.python.keras.utils.generic_utils import serialize_keras_object
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import while_loop
  from tensorflow.tools.docs import doc_controls
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