code: def l1_l2(l1=0.01, l2=0.01):  # pylint: disable=redefined-outer-name
      r"""Create a regularizer that applies both L1 and L2 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))`
  
      Args:
          l1: Float; L1 regularization factor.
          l2: Float; L2 regularization factor.
  
      Returns:
        An L1L2 Regularizer with the given regularization factors.
      """
      return L1L2(l1=l1, l2=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 3 for calling a global function with example usage: l1_l2(l1, l2) and returns: L1L2