code: def forward(self):
      a = torch.randn(3, 2)
      b = torch.rand(3, 2)
      c = torch.rand(3)
      log_probs = torch.randn(50, 16, 20).log_softmax(2).detach()
      targets = torch.randint(1, 20, (16, 30), dtype=torch.long)
      input_lengths = torch.full((16,), 50, dtype=torch.long)
      target_lengths = torch.randint(10, 30, (16,), dtype=torch.long)
      return len(
          F.binary_cross_entropy(torch.sigmoid(a), b),
          F.binary_cross_entropy_with_logits(torch.sigmoid(a), b),
          F.poisson_nll_loss(a, b),
          F.cosine_embedding_loss(a, b, c),
          F.cross_entropy(a, b),
          F.ctc_loss(log_probs, targets, input_lengths, target_lengths),
          # F.gaussian_nll_loss(a, b, torch.ones(5, 1)), # ENTER is not supported in mobile module
          F.hinge_embedding_loss(a, b),
          F.kl_div(a, b),
          F.l1_loss(a, b),
          F.mse_loss(a, b),
          F.margin_ranking_loss(c, c, c),
          F.multilabel_margin_loss(self.x, self.y),
          F.multilabel_soft_margin_loss(self.x, self.y),
          F.multi_margin_loss(self.x, torch.tensor([3])),
          F.nll_loss(a, torch.tensor([1, 0, 1])),
          F.huber_loss(a, b),
          F.smooth_l1_loss(a, b),
          F.soft_margin_loss(a, b),
          F.triplet_margin_loss(a, b, -b),
          # F.triplet_margin_with_distance_loss(a, b, -b), # can't take variable number of arguments
      )
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the NNLossFunctionModule obj's underlying member method code with example usage: obj.forward() and returns: len