class_docstr: Computes the self-adversarial binary cross entropy for negative sampling, from [1].
  
  [1] Z. Sun, Z.-H. Deng, J.-Y. Nie, and J. Tang, “RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space,” `arXiv:1902.10197 <http://arxiv.org/abs/1902.10197>`_
  
  Args:
      temperature (float, optional): a scaling factor for the weighting of negative samples
code: def __init__(
      self,
      temperature=1.0,
      name="self_adversarial_negative_sampling",
  ):
      self._temperature = temperature
      super().__init__(name=name)
  
imports: import tensorflow
  from .core.experimental import experimental
label_desc: Use image node_id 1 to create a new SelfAdversarialNegativeSampling object from inherited base classes: tf.keras.losses with example: obj = SelfAdversarialNegativeSampling(temperature, name)