code: def test_ctc_loss(self):
      torch.manual_seed(0)
  
      N = 16  # Batch size
      T = 50  # Input sequence length
      C = 20  # Number of classes (including blank)
      S = 30  # Target sequence length of longest target in batch (padding length)
      S_min = 10  # Minimum target length (only for testing)
  
      logits = torch.randn(N, T, C)
      targets = torch.randint(1, C, (N, S), dtype=torch.long)
      input_lengths = torch.full((N,), T, dtype=torch.long)
      target_lengths = torch.randint(S_min, S, (N,), dtype=torch.long)
  
      config = CTCLoss.Config()
      config.blank = 0  # Needs to be set to 0 for CuDNN support.
      ctc_loss_fn = CTCLoss(config=config)
  
      ctc_loss_val = ctc_loss_fn(
          logits,
          targets,
          input_lengths,
          target_lengths,
      )
  
      # PyTorch CTC loss
      log_probs = logits.permute(1, 0, 2).log_softmax(
          2
      )  # permute to conform to CTC loss input tensor (T,N,C) in PyTorch.
      lib_ctc_loss_val = F.ctc_loss(
          log_probs, targets, input_lengths, target_lengths
      )
  
      self.assertAlmostEqual(
          ctc_loss_val.item(), lib_ctc_loss_val.item()
      )
  
imports: import unittest
  import torch
  import torch.nn.functional
  from pytext.loss.loss import CTCLoss
label_desc: Use image node_id 1 for calling the CTCLossTest obj's underlying member method code with example usage: obj.test_ctc_loss() without return types