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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 train(self, plot):
 """ Compute all the required guides Parameters plot : ggplot ggplot object Returns ------- gdefs : list Guides for the plots """

        gdefs = []

        for scale in plot.scales:
            for output in scale.aesthetics:
                # The guide for aesthetic 'xxx' is stored
                # in plot.guides['xxx']. The priority for
                # the guides depends on how they are created
                # 1. ... + guides(xxx=guide_blah())
                # 2. ... + scale_xxx(guide=guide_blah())
                # 3. default(either guide_legend or guide_colorbar
                #            depending on the scale type)
                # output = scale.aesthetics[0]
                guide = self.get(output, scale.guide)

                if guide is None or guide is False:
                    continue

                # check the validity of guide.
                # if guide is character, then find the guide object
                guide = self.validate(guide)
                # check the consistency of the guide and scale.
                if (guide.available_aes != 'any' and
                        scale.aesthetics[0] not in guide.available_aes):
                    raise PlotnineError(
                        "{} cannot be used for {}".format(
                            guide.__class__.__name__, scale.aesthetics))

                # title
                if is_waive(guide.title):
                    if scale.name:
                        guide.title = scale.name
                    else:
                        try:
                            guide.title = str(plot.labels[output])
                        except KeyError:
                            warn("Cannot generate legend for the {!r} "
                                 "aesthetic. Make sure you have mapped a "
                                 "variable to it".format(output),
                                 PlotnineWarning)
                            continue

                # each guide object trains scale within the object,
                # so Guides (i.e., the container of guides)
                # need not to know about them
                guide = guide.train(scale, output)

                if guide is not None:
                    gdefs.append(guide)

        return gdefs