class_docstr: Computes the cross-entropy loss between true labels and predicted labels.
  
  Args:
      from_logits: Whether to interpret `y_pred` as a tensor of
      [logit](https://en.wikipedia.org/wiki/Logit) values. By default we assume
          that `y_pred` contains probabilities (i.e., values in [0, 1]).
code: def __init__(self, from_logits=False, lazy_normalization=False):
      self.from_logits = from_logits
      self.lazy_normalization = lazy_normalization
      if from_logits:
          super(BinaryCrossentropy, self).__init__(
              binary_crossentropy_from_logits
          )
      else:
          super(BinaryCrossentropy, self).__init__(binary_crossentropy)
  
imports: import tf_encrypted
label_desc: Use image node_id 1 to create a new BinaryCrossentropy object from inherited base classes: Loss with example: obj = BinaryCrossentropy(from_logits, lazy_normalization)