class_docstr: A regularizer that applies a L2 regularization penalty.
  
  The L2 regularization penalty is computed as:
  `loss = l2 * reduce_sum(square(x))`
  
  L2 may be passed to a layer as a string identifier:
  
  >>> dense = tf.keras.layers.Dense(3, kernel_regularizer='l2')
  
  In this case, the default value used is `l2=0.01`.
  
  Attributes:
      l2: Float; L2 regularization factor.
code: def __init__(
      self, l2=0.01, **kwargs
  ):  # pylint: disable=redefined-outer-name
      l2 = kwargs.pop("l", l2)  # Backwards compatibility
      if kwargs:
          raise TypeError("Argument(s) not recognized: %s" % (kwargs,))
  
      l2 = 0.01 if l2 is None else l2
      _check_penalty_number(l2)
  
      self.l2 = backend.cast_to_floatx(l2)
  
imports: import math
  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 math_ops
label_desc: Use image node_id 1 to create a new L2 object from inherited base classes: Regularizer with example: obj = L2(l2)