class_docstr: loss generator class is responsible for calculate loss for positive & negative loss / pairwise loss.
  It has different implementations of concrete method in regards of PyTorch and MXNet.
docstr: initialize BaseLossGenerator class
  
  Parameters
  ----------
  neg_adversarial_sampling : bool
      whether to use adversarial sampling for negative sample
  adversarial_temperature : float
      temperature value for adversarial sampling
  pairwise : bool
      whether the loss computed is pairwise or point wise
docstr_tok: ["initialize","BaseLossGenerator","class","Parameters","--","--","--","--","--","neg_adversarial_sampling",":","bool","whether","to","use","adversarial","sampling","for","negative","sample","adversarial_temperature",":","float","temperature","value","for","adversarial","sampling","pairwise",":","bool","whether","the","loss","computed","is","pairwise","or","point","wise"]
code: def __init__(
      self, neg_adversarial_sampling, adversarial_temperature, pairwise
  ):
      """initialize BaseLossGenerator class
  
      Parameters
      ----------
      neg_adversarial_sampling : bool
          whether to use adversarial sampling for negative sample
      adversarial_temperature : float
          temperature value for adversarial sampling
      pairwise : bool
          whether the loss computed is pairwise or point wise
      """
      self.pairwise = pairwise
      self.neg_adversarial_sampling = neg_adversarial_sampling
      if self.neg_adversarial_sampling:
          self.adversarial_temperature = adversarial_temperature
      else:
          self.adversarial_temperature = 0
      if (
          self.pairwise is True
          and self.neg_adversarial_sampling is True
      ):
          raise ValueError(
              "loss cannot be pairwise and adversarial sampled"
          )
  
label_desc: Use image node_id 1 to create a new BaseLossGenerator object from inherited base classes: object with example: obj = BaseLossGenerator(neg_adversarial_sampling, adversarial_temperature, pairwise)