def min(x, axis=None, keepdims=False, with_index=False, only_index=False):
    """Reduce the input N-D array `x` along the given `axis` using the min
    operation. The `axis` argument may be a single integer to reduce
    over one axis, a tuple of integers to reduce over multiple axes,
    or ``None`` to reduce over all axes. If `keepdims` is ``True``,
    the output will keep all reduced dimensions with size 1. If
    `with_index` is True, result is a tuple ``(sorted, indices)`` or
    only ``indices`` if `only_index` is True. Setting `only_index` to
    True implies that `with_index` is also True.

    .. code-block:: python

        import numpy as np
        import nnabla as nn
        import nnabla.functions as F

        nn.set_auto_forward(True)
        x = nn.Variable.from_numpy_array(np.random.rand(2, 3, 4))

        minval = F.min(x, axis=1)
        assert np.allclose(minval.d, np.min(x.d, axis=1))

        minval, indices = F.min(x, axis=1, with_index=True)
        assert np.allclose(minval.d, np.min(x.d, axis=1))
        assert np.all(indices.d == np.argmin(x.d, axis=1))

        indices = F.min(x, axis=1, only_index=True)
        assert np.all(indices.d == np.argmin(x.d, axis=1))

    Args:
        x (Variable): An input variable.
        axis (None, int or tuple of ints): Axis or axes along which min is
            calculated. The default value `None` will reduce all dimensions.
        keepdims(bool): Keep reduced axes as dimension with 1 element.
        with_index(bool): Return tuple of min values and index.
        only_index(bool): Return only the index of min values.

    Returns:
        ~nnabla.Variable: N-D array.

    """
    from .function_bases import min as min_base
    if axis is None:
        axis = range(x.ndim)
    elif not hasattr(axis, '__iter__'):
        axis = [axis]
    n_outputs = 2 if with_index and not only_index else 1
    return min_base(x, axis, keepdims, with_index, only_index, n_outputs)