code: def ctc_loss(
      target,
      output,
      target_length,
      output_length,
      mask_index=0,
  ):
      target = convert_to_tensor(target)
      output = convert_to_tensor(output)
      target_length = convert_to_tensor(target_length)
      output_length = convert_to_tensor(output_length)
  
      output = torch.transpose(output, 1, 0)
      logits = tnn.log_softmax(output, dim=-1)
  
      return tnn.ctc_loss(
          logits,
          target,
          output_length,
          target_length,
          blank=mask_index,
          reduction="none",
      )
  
imports: import torch
  import torch.nn.functional
  import tree
  from keras.backend import standardize_data_format
  from keras.backend import standardize_dtype
  from keras.backend.common.backend_utils import compute_conv_transpose_padding_args_for_torch
  from keras.backend.config import epsilon
  from keras.backend.torch.core import cast
  from keras.backend.torch.core import convert_to_tensor
  from keras.backend.torch.core import get_device
  from keras.backend.torch.numpy import expand_dims
  from keras.backend.torch.numpy import maximum
  from keras.backend.torch.numpy import where
  from keras.utils.argument_validation import standardize_tuple
label_desc: Use image node_id 35 for calling a global function with example usage: ctc_loss(target, output, target_length, output_length, mask_index) and returns: tnn