code: def stack(arrays: List[np.ndarray], *, axis: int = 0) -> np.ndarray:
      """Stack one or more arrays along a target axis.
  
      This function is similar to `np.stack`, except that memory contiguity is
      retained when stacking along the first dimension.
  
      This is useful when combining multiple monophonic audio signals into a
      multi-channel signal, or when stacking multiple feature representations
      to form a multi-dimensional array.
  
      Parameters
      ----------
      arrays : list
          one or more `np.ndarray`
      axis : integer
          The target axis along which to stack.  ``axis=0`` creates a new first axis,
          and ``axis=-1`` creates a new last axis.
  
      Returns
      -------
      arr_stack : np.ndarray [shape=(len(arrays), array_shape) or shape=(array_shape, len(arrays))]
          The input arrays, stacked along the target dimension.
  
          If ``axis=0``, then ``arr_stack`` will be F-contiguous.
          Otherwise, ``arr_stack`` will be C-contiguous by default, as computed by
          `np.stack`.
  
      Raises
      ------
      ParameterError
          - If ``arrays`` do not all have the same shape
          - If no ``arrays`` are given
  
      See Also
      --------
      numpy.stack
      numpy.ndarray.flags
      frame
  
      Examples
      --------
      Combine two buffers into a contiguous arrays
  
      >>> y_left = np.ones(5)
      >>> y_right = -np.ones(5)
      >>> y_stereo = librosa.util.stack([y_left, y_right], axis=0)
      >>> y_stereo
      array([[ 1.,  1.,  1.,  1.,  1.],
             [-1., -1., -1., -1., -1.]])
      >>> y_stereo.flags
        C_CONTIGUOUS : False
        F_CONTIGUOUS : True
        OWNDATA : True
        WRITEABLE : True
        ALIGNED : True
        WRITEBACKIFCOPY : False
        UPDATEIFCOPY : False
  
      Or along the trailing axis
  
      >>> y_stereo = librosa.util.stack([y_left, y_right], axis=-1)
      >>> y_stereo
      array([[ 1., -1.],
             [ 1., -1.],
             [ 1., -1.],
             [ 1., -1.],
             [ 1., -1.]])
      >>> y_stereo.flags
        C_CONTIGUOUS : True
        F_CONTIGUOUS : False
        OWNDATA : True
        WRITEABLE : True
        ALIGNED : True
        WRITEBACKIFCOPY : False
        UPDATEIFCOPY : False
      """
      shapes = {arr.shape for arr in arrays}
      if len(shapes) > 1:
          raise ParameterError(
              "all input arrays must have the same shape"
          )
      elif len(shapes) < 1:
          raise ParameterError(
              "at least one input array must be provided for stack"
          )
  
      shape_in = shapes.pop()
  
      if axis != 0:
          return np.stack(arrays, axis=axis)
      else:
          # If axis is 0, enforce F-ordering
          shape = tuple([len(arrays)] + list(shape_in))
  
          # Find the common dtype for all inputs
          dtype = np.result_type(*arrays)
  
          # Allocate an empty array of the right shape and type
          result = np.empty(shape, dtype=dtype, order="F")
  
          # Stack into the preallocated buffer
          np.stack(arrays, axis=axis, out=result)
  
          return result
  
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 34 for calling a global function with example usage: stack(arrays) and returns: np, result