code: def dtw_cuda(x, BLOCK_SIZE=1024):
      from .triton_ops import dtw_kernel
  
      M, N = x.shape
      assert M < BLOCK_SIZE, f"M should be smaller than {BLOCK_SIZE=}"
  
      x_skew = (
          F.pad(x, (0, M + 1), value=np.inf)
          .flatten()[: M * (N + M)]
          .reshape(M, N + M)
      )
      x_skew = x_skew.T.contiguous()
      cost = torch.ones(N + M + 2, M + 2) * np.inf
      cost[0, 0] = 0
      cost = cost.cuda()
      trace = torch.zeros_like(cost, dtype=torch.int32)
  
      dtw_kernel[(1,)](
          cost,
          trace,
          x_skew,
          x_skew.stride(0),
          cost.stride(0),
          trace.stride(0),
          N,
          M,
          BLOCK_SIZE=BLOCK_SIZE,
      )
  
      trace = trace.T.flatten()[: (M + 1) * (M + N + 3)].reshape(
          M + 1, M + N + 3
      )[:, : N + 1]
      return backtrace(trace.cpu().numpy())
  
file_path: whisper/whisper/timing.py
imports: import itertools
  import subprocess
  import warnings
  from dataclasses import dataclass
  from typing import TYPE_CHECKING, List
  import numba
  import numpy
  import torch
  import torch.nn.functional
  from .audio import HOP_LENGTH, SAMPLE_RATE, TOKENS_PER_SECOND
  from .tokenizer import Tokenizer
label_desc: Use image node_id 4 for calling a global function with example usage: dtw_cuda(x, BLOCK_SIZE) and returns: backtrace