class_docstr: Strong Convex version of Huber loss using l2 weight regularization.
class_docstr_tok: ["Strong","Convex","version","of","Huber","loss","using","l2","weight","regularization","."]
docstr: Constructor.
  
  Args:
    reg_lambda: Weight regularization constant
    c_arg: Penalty parameter C of the loss term
    radius_constant: constant defining the length of the radius
    delta: delta value in huber loss.  When to switch from quadratic to
      absolute deviation.
    reduction: reduction type to use. See super class
    dtype: tf datatype to use for tensor conversions.
  
  Returns:
    Loss values per sample.
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code: def __init__(
      self,
      reg_lambda,
      c_arg,
      radius_constant,
      delta,
      reduction=losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE,
      dtype=tf.float32,
  ):
      """Constructor.
  
      Args:
        reg_lambda: Weight regularization constant
        c_arg: Penalty parameter C of the loss term
        radius_constant: constant defining the length of the radius
        delta: delta value in huber loss.  When to switch from quadratic to
          absolute deviation.
        reduction: reduction type to use. See super class
        dtype: tf datatype to use for tensor conversions.
  
      Returns:
        Loss values per sample.
      """
      if c_arg <= 0:
          raise ValueError("c: {0}, should be >= 0".format(c_arg))
      if reg_lambda <= 0:
          raise ValueError(
              "reg lambda: {0} must be positive".format(reg_lambda)
          )
      if radius_constant <= 0:
          raise ValueError(
              "radius_constant: {0}, should be >= 0".format(
                  radius_constant
              )
          )
      if delta <= 0:
          raise ValueError("delta: {0}, should be >= 0".format(delta))
      self.C = c_arg  # pylint: disable=invalid-name
      self.delta = delta
      self.radius_constant = radius_constant
      self.dtype = dtype
      self.reg_lambda = tf.constant(reg_lambda, dtype=self.dtype)
      super().__init__(reduction=reduction, name="strongconvexhuber")
  
imports: import tensorflow.compat.v1
  from tensorflow.compat.v1.python.framework import ops
  from tensorflow.compat.v1.python.keras import losses
  from tensorflow.compat.v1.python.keras.regularizers import L1L2
  from tensorflow.compat.v1.python.keras.utils import losses_utils
  from tensorflow.compat.v1.python.platform import tf_logging
label_desc: Use image node_id 1 to create a new StrongConvexHuber object from inherited base classes: losses, StrongConvexMixin with example: obj = StrongConvexHuber(reg_lambda, c_arg, radius_constant, delta, reduction, dtype)