code: def __init__(
      self,
      args,
      loss_genre="Logsigmoid",
      neg_adversarial_sampling=False,
      adversarial_temperature=1.0,
      pairwise=False,
  ):
      super(LossGenerator, self).__init__(
          neg_adversarial_sampling, adversarial_temperature, pairwise
      )
      if loss_genre == "Hinge":
          self.neg_label = -1
          self.loss_criterion = HingeLoss(args.margin)
      elif loss_genre == "Logistic":
          self.neg_label = -1
          self.loss_criterion = LogisticLoss()
      elif loss_genre == "Logsigmoid":
          self.neg_label = -1
          self.loss_criterion = LogsigmoidLoss()
      elif loss_genre == "BCE":
          self.neg_label = 0
          self.loss_criterion = BCELoss()
      else:
          raise ValueError("loss genre %s is not support" % loss_genre)
  
      if self.pairwise and loss_genre not in ["Logistic", "Hinge"]:
          raise ValueError(
              "{} loss cannot be applied to pairwise loss function".format(
                  loss_genre
              )
          )
  
imports: from ..base_loss import *
  from .tensor_models import *
  import torch
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new LossGenerator object from inherited base classes: BaseLossGenerator with example: obj = LossGenerator(args, loss_genre, neg_adversarial_sampling, adversarial_temperature, pairwise)