code: def forward(
      ctx,
      x,
      label,
      f_len,
      y_len,
      batch_offset,
      max_f_len,
      blank_idx,
      fuse_softmax_backward,
      debug_list,
      opt,
      packed_input,
  ):
      if fuse_softmax_backward == False:
          with torch.enable_grad():
              x = torch.nn.functional.log_softmax(x, dim=-1)
      else:
          x = torch.nn.functional.log_softmax(x, dim=-1)
      alpha, beta, loss = transducer_loss_cuda.forward(
          x,
          label,
          f_len,
          y_len,
          batch_offset,
          max_f_len,
          blank_idx,
          opt,
          packed_input,
      )
      if debug_list == []:
          debug_list += [alpha, beta]
      ctx.save_for_backward(
          x, alpha, beta, f_len, y_len, label, batch_offset
      )
      ctx.blank_idx = blank_idx
      ctx.fuse_softmax_backward = fuse_softmax_backward
      ctx.opt = opt
      ctx.packed_input = packed_input
      ctx.max_f_len = max_f_len
      return loss
  
file_path: apex/apex/contrib/transducer/transducer.py
imports: import torch
  import transducer_loss_cuda
  import transducer_joint_cuda
label_desc: Use image node_id 1 for calling the TransducerLossFunc obj's underlying member method code with example usage: obj.forward(ctx, x, label, f_len, y_len, batch_offset, max_f_len, blank_idx, fuse_softmax_backward, debug_list, opt, packed_input) and returns: loss