code: def prep_padded_densefsavec(
      log_softmax: torch.Tensor, supervisions: torch.Tensor
  ) -> "k2.DenseFsaVec":
      """Performs special epsilon-padding required for composition with some of the topologies."""
      log_softmax_eps = torch.cat(
          [
              log_softmax,
              torch.full(
                  (log_softmax.shape[0], log_softmax.shape[1], 1),
                  -float("inf"),
                  device=log_softmax.device,
              ),
          ],
          axis=-1,
      )
      log_softmax_padded = torch.zeros(
          (
              log_softmax_eps.shape[0],
              log_softmax_eps.shape[1] * 2,
              log_softmax_eps.shape[2],
          ),
          device=log_softmax.device,
      )
      log_softmax_padded[:, ::2] = log_softmax_eps
      supervisions_padded = supervisions.clone()
      supervisions_padded[:, 2] *= 2
      dense_log_softmax_padded = k2.DenseFsaVec(
          log_softmax_padded, supervisions_padded
      )
      return dense_log_softmax_padded
  
imports: import os
  import struct
  from pickle import UnpicklingError
  from typing import List, Optional, Tuple, Union
  import torch
  from nemo.core.utils.k2_guard import k2
  from nemo.utils import logging
label_desc: Use image node_id 10 for calling a global function with example usage: prep_padded_densefsavec(log_softmax, supervisions) and returns: dense_log_softmax_padded