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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 plot_projections(x, relative_to=None, autolim=True, axes=None, subplots_kwargs=dict(), labels=None, plot_function=None, **kwargs):
 """ Given N-dimensional quantity, ``x``, make a figure containing 2D projections of all combinations of the axes. Parameters x : array_like Array of values. ``axis=0`` is assumed to be the dimensionality, ``axis=1`` is the time axis. See :ref:`shape-conventions` for more information. relative_to : bool (optional) Plot the values relative to this value or values. autolim : bool (optional) Automatically set the plot limits to be something sensible. axes : array_like (optional) Array of matplotlib Axes objects. subplots_kwargs : dict (optional) Dictionary of kwargs passed to :func:`~matplotlib.pyplot.subplots`. labels : iterable (optional) List or iterable of axis labels as strings. They should correspond to the dimensions of the input orbit. plot_function : callable (optional) The ``matplotlib`` plot function to use. By default, this is :func:`~matplotlib.pyplot.scatter`, but can also be, e.g., :func:`~matplotlib.pyplot.plot`. **kwargs All other keyword arguments are passed to the ``plot_function``. Returns ------- fig : `~matplotlib.Figure` """


    # don't propagate changes back...
    x = np.array(x, copy=True)
    ndim = x.shape[0]

    # get axes object from arguments
    if axes is None:
        axes = _get_axes(dim=ndim, subplots_kwargs=subplots_kwargs)

    # if the quantities are relative
    if relative_to is not None:
        x -= relative_to

    # name of the plotting function
    plot_fn_name = plot_function.__name__

    # automatically determine limits
    if autolim:
        lims = []
        for i in range(ndim):
            max_,min_ = np.max(x[i]), np.min(x[i])
            delta = max_ - min_

            if delta == 0.:
                delta = 1.

            lims.append([min_ - delta*0.02, max_ + delta*0.02])

    k = 0
    for i in range(ndim):
        for j in range(ndim):
            if i >= j:
                continue # skip diagonal, upper triangle

            plot_func = getattr(axes[k], plot_fn_name)
            plot_func(x[i], x[j], **kwargs)

            if labels is not None:
                axes[k].set_xlabel(labels[i])
                axes[k].set_ylabel(labels[j])

            if autolim:
                axes[k].set_xlim(lims[i])
                axes[k].set_ylim(lims[j])

            k += 1

    axes[0].figure.tight_layout()
    return axes[0].figure