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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, scale, aesthetic=None):
 """ Create the key for the guide The key is a dataframe with two columns: - scale name : values - label : labels for each value scale name is one of the aesthetics ['x', 'y', 'color', 'fill', 'size', 'shape', 'alpha', 'stroke'] Returns this guide if training is successful and None if it fails """

        if aesthetic is None:
            aesthetic = scale.aesthetics[0]

        breaks = scale.get_breaks()
        if isinstance(breaks, OrderedDict):
            if all([np.isnan(x) for x in breaks.values()]):
                return None
        elif not len(breaks) or all(np.isnan(breaks)):
            return None

        with suppress(AttributeError):
            breaks = list(breaks.keys())

        key = pd.DataFrame({
            aesthetic: scale.map(breaks),
            'label': scale.get_labels(breaks)})
        # Drop out-of-range values for continuous scale
        # (should use scale$oob?)

        # Currently, numpy does not deal with NA (Not available)
        # When they are introduced, the block below should be
        # adjusted likewise, see ggplot2, guide-lengend.r
        if isinstance(scale, scale_continuous):
            limits = scale.limits
            b = np.asarray(breaks)
            noob = np.logical_and(limits[0] <= b,
                                  b <= limits[1])
            key = key[noob]

        if len(key) == 0:
            return None

        self.key = key

        # create a hash of the important information in the guide
        labels = ' '.join(str(x) for x in self.key['label'])
        info = '\n'.join([self.title, labels, str(self.direction),
                          self.__class__.__name__])
        self.hash = hashlib.md5(info.encode('utf-8')).hexdigest()
        return self