class_docstr: Strongly Convex BinaryCrossentropy loss using l2 weight regularization.
class_docstr_tok: ["Strongly","Convex","BinaryCrossentropy","loss","using","l2","weight","regularization","."]
docstr: Computes loss.
  
  Args:
    y_true: Ground truth values.
    y_pred: The predicted values.
  
  Returns:
    Loss values per sample.
docstr_tok: ["Computes","loss",".","Args",":","y_true",":","Ground","truth","values",".","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.
        y_pred: The predicted values.
  
      Returns:
        Loss values per sample.
      """
      loss = super(StrongConvexBinaryCrossentropy, self).call(
          y_true, y_pred
      )
      loss = loss * self.C
      return loss
  
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 StrongConvexBinaryCrossentropy obj's underlying member method code with example usage: obj.call(y_true, y_pred) and returns: loss