code: def _intersect_device(
      a_fsas: k2.Fsa,
      b_fsas: k2.Fsa,
      b_to_a_map: torch.Tensor,
      sorted_match_a: bool,
      batch_size: int = 500,
  ):
      """Wrap k2.intersect_device
  
      This is a wrapper of k2.intersect_device and its purpose is to split
      b_fsas into several batches and process each batch separately to avoid
      CUDA OOM error.
      The arguments and return value of this function are the same as
      k2.intersect_device.
  
      NOTE: You can decrease batch_size in case of CUDA out of memory error.
      """
      assert (
          k2 is not None
      ), "please follow 'tools/installers' to install"
  
      num_fsas = b_fsas.shape[0]
      if num_fsas <= batch_size:
          return k2.intersect_device(
              a_fsas,
              b_fsas,
              b_to_a_map=b_to_a_map,
              sorted_match_a=sorted_match_a,
          )
  
      num_batches = int(math.ceil(float(num_fsas) / batch_size))
      splits = []
      for i in range(num_batches):
          start = i * batch_size
          end = min(start + batch_size, num_fsas)
          splits.append((start, end))
  
      ans = []
      for start, end in splits:
          indexes = torch.arange(start, end).to(b_to_a_map)
  
          fsas = k2.index_fsa(b_fsas, indexes)
          b_to_a = k2.index_select(b_to_a_map, indexes)
          path_lats = k2.intersect_device(
              a_fsas,
              fsas,
              b_to_a_map=b_to_a,
              sorted_match_a=sorted_match_a,
          )
          ans.append(path_lats)
  
      return k2.cat(ans)
  
imports: import math
  from typing import List, Tuple
  import torch
label_desc: Use image node_id 2 for calling a global function with example usage: _intersect_device(a_fsas, b_fsas, b_to_a_map, sorted_match_a, batch_size) and returns: k2, k2