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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:

def get_clean_interp_index(arr, dim, use_coordinate=True, **kwargs):
    '''get index to use for x values in interpolation.

    If use_coordinate is True, the coordinate that shares the name of the
    dimension along which interpolation is being performed will be used as the
    x values.

    If use_coordinate is False, the x values are set as an equally spaced
    sequence.
    '''
    if use_coordinate:
        if use_coordinate is True:
            index = arr.get_index(dim)
        else:
            index = arr.coords[use_coordinate]
            if index.ndim != 1:
                raise ValueError(
                    'Coordinates used for interpolation must be 1D, '
                    '%s is %dD.' % (use_coordinate, index.ndim))

        # raise if index cannot be cast to a float (e.g. MultiIndex)
        try:
            index = index.values.astype(np.float64)
        except (TypeError, ValueError):
            # pandas raises a TypeError
            # xarray/nuppy raise a ValueError
            raise TypeError('Index must be castable to float64 to support'
                            'interpolation, got: %s' % type(index))
        # check index sorting now so we can skip it later
        if not (np.diff(index) > 0).all():
            raise ValueError("Index must be monotonicly increasing")
    else:
        axis = arr.get_axis_num(dim)
        index = np.arange(arr.shape[axis], dtype=np.float64)

    return index