code: def sliding_window_view(
      x, window_shape, axis=None, *, subok=False, writeable=False
  ):
      """
      Create a sliding window view into the array with the given window shape.
  
      Also known as rolling or moving window, the window slides across all
      dimensions of the array and extracts subsets of the array at all window
      positions.
  
  
      Parameters
      ----------
      x : array_like
          Array to create the sliding window view from.
      window_shape : int or tuple of int
          Size of window over each axis that takes part in the sliding window.
          If `axis` is not present, must have same length as the number of input
          array dimensions. Single integers `i` are treated as if they were the
          tuple `(i,)`.
      axis : int or tuple of int, optional
          Axis or axes along which the sliding window is applied.
          By default, the sliding window is applied to all axes and
          `window_shape[i]` will refer to axis `i` of `x`.
          If `axis` is given as a `tuple of int`, `window_shape[i]` will refer to
          the axis `axis[i]` of `x`.
          Single integers `i` are treated as if they were the tuple `(i,)`.
      subok : bool, optional
          If True, sub-classes will be passed-through, otherwise the returned
          array will be forced to be a base-class array (default).
      writeable : bool, optional -- not supported
          When true, allow writing to the returned view. The default is false,
          as this should be used with caution: the returned view contains the
          same memory location multiple times, so writing to one location will
          cause others to change.
  
      Returns
      -------
      view : ndarray
          Sliding window view of the array. The sliding window dimensions are
          inserted at the end, and the original dimensions are trimmed as
          required by the size of the sliding window.
          That is, ``view.shape = x_shape_trimmed + window_shape``, where
          ``x_shape_trimmed`` is ``x.shape`` with every entry reduced by one less
          than the corresponding window size.
  
  
      See also
      --------
      numpy.lib.stride_tricks.as_strided
  
      Notes
      --------
      This function is adapted from numpy.lib.stride_tricks.as_strided.
  
      Examples
      --------
      >>> x = _cupy.arange(6)
      >>> x.shape
      (6,)
      >>> v = sliding_window_view(x, 3)
      >>> v.shape
      (4, 3)
      >>> v
      array([[0, 1, 2],
             [1, 2, 3],
             [2, 3, 4],
             [3, 4, 5]])
  
      """
  
      window_shape = (
          tuple(window_shape)
          if np.iterable(window_shape)
          else (window_shape,)
      )
  
      # writeable is not supported:
      if writeable:
          raise NotImplementedError(
              "Writeable views are not supported."
          )
  
      # first convert input to array, possibly keeping subclass
      x = _cupy.array(x, copy=False, subok=subok)
  
      window_shape_array = _cupy.array(window_shape)
      for dim in window_shape_array:
          if dim < 0:
              raise ValueError(
                  "`window_shape` cannot contain negative values"
              )
  
      if axis is None:
          axis = tuple(range(x.ndim))
          if len(window_shape) != len(axis):
              raise ValueError(
                  f"Since axis is `None`, must provide "
                  f"window_shape for all dimensions of `x`; "
                  f"got {len(window_shape)} window_shape elements "
                  f"and `x.ndim` is {x.ndim}."
              )
      else:
          axis = _cupy._core.internal._normalize_axis_indices(
              axis, x.ndim
          )
          if len(window_shape) != len(axis):
              raise ValueError(
                  f"Must provide matching length window_shape and "
                  f"axis; got {len(window_shape)} window_shape "
                  f"elements and {len(axis)} axes elements."
              )
  
      out_strides = x.strides + tuple(x.strides[ax] for ax in axis)
  
      # note: same axis can be windowed repeatedly
      x_shape_trimmed = list(x.shape)
      for ax, dim in zip(axis, window_shape):
          if x_shape_trimmed[ax] < dim:
              raise ValueError(
                  "window shape cannot be larger than input array shape"
              )
          x_shape_trimmed[ax] -= dim - 1
      out_shape = tuple(x_shape_trimmed) + window_shape
      return as_strided(x, strides=out_strides, shape=out_shape)
  
label_desc: Use image node_id 2 for calling a global function with example usage: sliding_window_view(x, window_shape, axis) and returns: as_strided