class_docstr: A regularizer that applies both L1 and L2 regularization penalties.
  
  The L1 regularization penalty is computed as:
  `loss = l1 * reduce_sum(abs(x))`
  
  The L2 regularization penalty is computed as
  `loss = l2 * reduce_sum(square(x))`
  
  L1L2 may be passed to a layer as a string identifier:
  
  >>> dense = tf.keras.layers.Dense(3, kernel_regularizer='l1_l2')
  
  In this case, the default values used are `l1=0.01` and `l2=0.01`.
  
  Attributes:
      l1: Float; L1 regularization factor.
      l2: Float; L2 regularization factor.
code: def __call__(self, x):
      regularization = backend.constant(0.0, dtype=x.dtype)
      if self.l1:
          regularization += self.l1 * math_ops.reduce_sum(
              math_ops.abs(x)
          )
      if self.l2:
          regularization += self.l2 * math_ops.reduce_sum(
              math_ops.square(x)
          )
      return regularization
  
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 2 for calling the L1L2 obj's underlying member method code with example usage: obj.__call__(x) and returns: regularization