def METHOD_NAME(
    random_state,
    low,
    high=None,
    size=None,
    dtype="l",
    density=None,
    chunk_size=None,
    gpu=None,
):
    """
    Return random integers from `low` (inclusive) to `high` (exclusive).
    Return random integers from the "discrete uniform" distribution of
    the specified dtype in the "half-open" interval [`low`, `high`). If
    `high` is None (the default), then results are from [0, `low`).
    Parameters
    ----------
    low : int
        Lowest (signed) integer to be drawn from the distribution (unless
        ``high=None``, in which case this parameter is one above the
        *highest* such integer).
    high : int, optional
        If provided, one above the largest (signed) integer to be drawn
        from the distribution (see above for behavior if ``high=None``).
    size : int or tuple of ints, optional
        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
        ``m * n * k`` samples are drawn.  Default is None, in which case a
        single value is returned.
    dtype : dtype, optional
        Desired dtype of the result. All dtypes are determined by their
        name, i.e., 'int64', 'int', etc, so byteorder is not available
        and a specific precision may have different C types depending
        on the platform. The default value is 'np.int'.
    density: float, optional
        if density specified, a sparse tensor will be created
    chunk_size : int or tuple of int or tuple of ints, optional
        Desired chunk size on each dimension
    gpu : bool, optional
        Allocate the tensor on GPU if True, False as default
    dtype : data-type, optional
      Data-type of the returned tensor.
    Returns
    -------
    out : int or Tensor of ints
        `size`-shaped tensor of random integers from the appropriate
        distribution, or a single such random int if `size` not provided.
    See Also
    --------
    random.random_integers : similar to `randint`, only for the closed
        interval [`low`, `high`], and 1 is the lowest value if `high` is
        omitted. In particular, this other one is the one to use to generate
        uniformly distributed discrete non-integers.
    Examples
    --------
    >>> import mars.tensor as mt
    >>> mt.random.randint(2, size=10).execute()
    array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])
    >>> mt.random.randint(1, size=10).execute()
    array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
    Generate a 2 x 4 tensor of ints between 0 and 4, inclusive:
    >>> mt.random.randint(5, size=(2, 4)).execute()
    array([[4, 0, 2, 1],
           [3, 2, 2, 0]])
    """
    sparse = bool(density)
    size = random_state._handle_size(size)
    seed = gen_random_seeds(1, random_state.to_numpy())[0]
    op = TensorRandint(
        seed=seed,
        low=low,
        high=high,
        size=size,
        dtype=dtype,
        gpu=gpu,
        sparse=sparse,
        density=density,
    )
    return op(chunk_size=chunk_size)