code: def index_to_slice(
      idx: _SequenceLike[int],
      *,
      idx_min: Optional[int] = None,
      idx_max: Optional[int] = None,
      step: Optional[int] = None,
      pad: bool = True,
  ) -> List[slice]:
      """Generate a slice array from an index array.
  
      Parameters
      ----------
      idx : list-like
          Array of index boundaries
      idx_min, idx_max : None or int
          Minimum and maximum allowed indices
      step : None or int
          Step size for each slice.  If `None`, then the default
          step of 1 is used.
      pad : boolean
          If `True`, pad ``idx`` to span the range ``idx_min:idx_max``.
  
      Returns
      -------
      slices : list of slice
          ``slices[i] = slice(idx[i], idx[i+1], step)``
          Additional slice objects may be added at the beginning or end,
          depending on whether ``pad==True`` and the supplied values for
          ``idx_min`` and ``idx_max``.
  
      See Also
      --------
      fix_frames
  
      Examples
      --------
      >>> # Generate slices from spaced indices
      >>> librosa.util.index_to_slice(np.arange(20, 100, 15))
      [slice(20, 35, None), slice(35, 50, None), slice(50, 65, None), slice(65, 80, None),
       slice(80, 95, None)]
      >>> # Pad to span the range (0, 100)
      >>> librosa.util.index_to_slice(np.arange(20, 100, 15),
      ...                             idx_min=0, idx_max=100)
      [slice(0, 20, None), slice(20, 35, None), slice(35, 50, None), slice(50, 65, None),
       slice(65, 80, None), slice(80, 95, None), slice(95, 100, None)]
      >>> # Use a step of 5 for each slice
      >>> librosa.util.index_to_slice(np.arange(20, 100, 15),
      ...                             idx_min=0, idx_max=100, step=5)
      [slice(0, 20, 5), slice(20, 35, 5), slice(35, 50, 5), slice(50, 65, 5), slice(65, 80, 5),
       slice(80, 95, 5), slice(95, 100, 5)]
      """
      # First, normalize the index set
      idx_fixed = fix_frames(idx, x_min=idx_min, x_max=idx_max, pad=pad)
  
      # Now convert the indices to slices
      return [
          slice(start, end, step)
          for (start, end) in zip(idx_fixed, idx_fixed[1:])
      ]
  
imports: from __future__ import annotations
  import scipy.ndimage
  import scipy.sparse
  import numpy
  import numba
  from numpy.lib.stride_tricks import as_strided
  from .._cache import cache
  from .exceptions import ParameterError
  from .deprecation import Deprecated
  from numpy.typing import ArrayLike, DTypeLike
  from typing import Any, Callable, Iterable, List, Dict, Optional, Sequence, Tuple, TypeVar, Union, overload
  from typing_extensions import Literal
  from .._typing import _SequenceLike, _FloatLike_co, _ComplexLike_co
label_desc: Use image node_id 23 for calling a global function with example usage: index_to_slice(idx) and returns: unknown