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: Computes loss.
  
  Args:
    y_true: Ground truth values. One hot encoded using -1 and 1.
    y_pred: The predicted values.
  
  Returns:
    Loss values per sample.
docstr_tok: ["Computes","loss",".","Args",":","y_true",":","Ground","truth","values",".","One","hot","encoded","using","-1","and","1.","y_pred",":","The","predicted","values",".","Returns",":","Loss","values","per","sample","."]
code: def call(self, y_true, y_pred):
      """Computes loss.
  
      Args:
        y_true: Ground truth values. One hot encoded using -1 and 1.
        y_pred: The predicted values.
  
      Returns:
        Loss values per sample.
      """
      h = self.delta
      z = y_pred * y_true
      one = tf.constant(1, dtype=self.dtype)
      four = tf.constant(4, dtype=self.dtype)
  
      if z > one + h:  # pylint: disable=no-else-return
          return _ops.convert_to_tensor_v2(0, dtype=self.dtype)
      elif tf.math.abs(one - z) <= h:
          return one / (four * h) * tf.math.pow(one + h - z, 2)
      return one - z
  
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 2 for calling the StrongConvexHuber obj's underlying member method code with example usage: obj.call(y_true, y_pred) and returns: unknown, _ops, unknown