class_docstr: Creates a criterion that measures the Binary Cross Entropy between the
  ground truth (gt) and the prediction (pr).
  
  .. math:: L(gt, pr) = - gt \cdot \log(pr) - (1 - gt) \cdot \log(1 - pr)
  
  Returns:
      A callable ``binary_crossentropy`` instance. Can be used in ``model.compile(...)`` function
      or combined with other losses.
  
  Example:
  
  .. code:: python
  
      loss = BinaryCELoss()
      model.compile('SGD', loss=loss)
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code: def __init__(self):
      super().__init__(name="binary_crossentropy")
  
imports: from .base import Loss
  from .base import functional
label_desc: Use image node_id 1 to create a new BinaryCELoss object from inherited base classes: Loss with example: obj = BinaryCELoss()