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: Instantiates a weight constraint from a configuration dictionary.
  
  Example:
  
  ```python
  constraint = UnitNorm()
  config = constraint.get_config()
  constraint = UnitNorm.from_config(config)
  ```
  
  Args:
      config: A Python dictionary, the output of `get_config()`.
  
  Returns:
      A `keras.constraints.Constraint` instance.
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code: def from_config(cls, config):
      """Instantiates a weight constraint from a configuration dictionary.
  
      Example:
  
      ```python
      constraint = UnitNorm()
      config = constraint.get_config()
      constraint = UnitNorm.from_config(config)
      ```
  
      Args:
          config: A Python dictionary, the output of `get_config()`.
  
      Returns:
          A `keras.constraints.Constraint` instance.
      """
      return cls(**config)
  
imports: from keras import backend
  from keras import ops
  from keras.api_export import keras_export
label_desc: Use image node_id 3 for calling the Constraint obj's underlying member method code with example usage: obj.from_config(cls, config) and returns: cls