code: def dtw_backtracking(
      steps: np.ndarray,
      *,
      step_sizes_sigma: Optional[np.ndarray] = None,
      subseq: bool = False,
      start: Optional[Union[int, np.integer[Any]]] = None,
  ) -> np.ndarray:
      """Backtrack a warping path.
  
      Uses the saved step sizes from the cost accumulation
      step to backtrack the index pairs for a warping path.
  
      Parameters
      ----------
      steps : np.ndarray [shape=(N, M)]
          Step matrix, containing the indices of the used steps from the cost
          accumulation step.
      step_sizes_sigma : np.ndarray [shape=[n, 2]]
          Specifies allowed step sizes as used by the dtw.
      subseq : bool
          Enable subsequence DTW, e.g., for retrieval tasks.
      start : int
          Start column index for backtraing (only allowed for ``subseq=True``)
  
      Returns
      -------
      wp : list [shape=(N,)]
          Warping path with index pairs.
          Each list entry contains an index pair
          (n, m) as a tuple
  
      See Also
      --------
      dtw
      """
      if subseq is False and start is not None:
          raise ParameterError(
              f"start is only allowed to be set if subseq is True (start={start}, subseq={subseq})"
          )
  
      # Default Parameters
      default_steps = np.array(
          [[1, 1], [0, 1], [1, 0]], dtype=np.uint32
      )
  
      if step_sizes_sigma is None:
          # Use the default steps
          step_sizes_sigma = default_steps
      else:
          # Append custom steps and weights to our defaults
          step_sizes_sigma = np.concatenate(
              (default_steps, step_sizes_sigma)
          )
  
      wp = __dtw_backtracking(steps, step_sizes_sigma, subseq, start)
      return np.asarray(wp, dtype=int)
  
imports: from __future__ import annotations
  import numpy
  from scipy.spatial.distance import cdist
  from numba import jit
  from .util import pad_center, fill_off_diagonal, is_positive_int, tiny, expand_to
  from .util.exceptions import ParameterError
  from .filters import get_window
  from typing import Any, Iterable, List, Optional, Tuple, Union, overload
  from typing_extensions import Literal
  from ._typing import _WindowSpec, _IntLike_co
label_desc: Use image node_id 12 for calling a global function with example usage: dtw_backtracking(steps) and returns: np