def tile(
    A: ndarray, reps: int | Sequence[int] | npt.NDArray[np.int_]
) -> ndarray:
    """
    Construct an array by repeating A the number of times given by reps.

    If `reps` has length ``d``, the result will have dimension of ``max(d,
    A.ndim)``.

    If ``A.ndim < d``, `A` is promoted to be d-dimensional by prepending new
    axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication,
    or shape (1, 1, 3) for 3-D replication. If this is not the desired
    behavior, promote `A` to d-dimensions manually before calling this
    function.

    If ``A.ndim > d``, `reps` is promoted to `A`.ndim by pre-pending 1's to it.
    Thus for an `A` of shape (2, 3, 4, 5), a `reps` of (2, 2) is treated as
    (1, 1, 2, 2).

    Parameters
    ----------
    A : array_like
        The input array.
    reps : 1d array_like
        The number of repetitions of `A` along each axis.

    Returns
    -------
    c : ndarray
        The tiled output array.

    See Also
    --------
    numpy.tile

    Availability
    --------
    Multiple GPUs, Multiple CPUs
    """
    computed_reps: tuple[int, ...]
    if isinstance(reps, int):
        computed_reps = (reps,)
    else:
        if np.ndim(reps) > 1:
            raise TypeError("`reps` must be a 1d sequence")
        computed_reps = tuple(reps)
    # Figure out the shape of the destination array
    out_dims = _builtin_max(A.ndim, len(computed_reps))
    # Prepend ones until the dimensions match
    while len(computed_reps) < out_dims:
        computed_reps = (1,) + computed_reps
    out_shape: NdShape = ()
    # Prepend dimensions if necessary
    for dim in range(out_dims - A.ndim):
        out_shape += (computed_reps[dim],)
    offset = len(out_shape)
    for dim in range(A.ndim):
        out_shape += (A.shape[dim] * computed_reps[offset + dim],)
    assert len(out_shape) == out_dims
    result = ndarray(out_shape, dtype=A.dtype, inputs=(A,))
    result._thunk.tile(A._thunk, computed_reps)
    return result

    Construct an array by repeating A the number of times given by reps.

    If `reps` has length ``d``, the result will have dimension of ``max(d,
    A.ndim)``.

    If ``A.ndim < d``, `A` is promoted to be d-dimensional by prepending new
    axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication,
    or shape (1, 1, 3) for 3-D replication. If this is not the desired
    behavior, promote `A` to d-dimensions manually before calling this
    function.

    If ``A.ndim > d``, `reps` is promoted to `A`.ndim by pre-pending 1's to it.
    Thus for an `A` of shape (2, 3, 4, 5), a `reps` of (2, 2) is treated as
    (1, 1, 2, 2).

    Parameters
    ----------
    A : array_like
        The input array.
    reps : 1d array_like
        The number of repetitions of `A` along each axis.

    Returns
    -------
    c : ndarray
        The tiled output array.

    See Also
    --------
    numpy.tile

    Availability
    --------
    Multiple GPUs, Multiple CPUs
    
cupynumeric/_module/array_tiling.py