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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 most_similar(self, items, num=10, batch_size=100, show_progressbar=False, return_names=True):
 """ Return the num most similar items to a given list of items. Parameters items : list of objects or a single object. The items to get the most similar items to. num : int, optional, default 10 The number of most similar items to retrieve. batch_size : int, optional, default 100. The batch size to use. 100 is a good default option. Increasing the batch size may increase the speed. show_progressbar : bool, optional, default False Whether to show a progressbar. return_names : bool, optional, default True Whether to return the item names, or just the distances. Returns ------- sim : array For each items in the input the num most similar items are returned in the form of (NAME, DISTANCE) tuples. If return_names is false, the returned list just contains distances. """

        # This line allows users to input single items.
        # We used to rely on string identities, but we now also allow
        # anything hashable as keys.
        # Might fail if a list of passed items is also in the vocabulary.
        # but I can't think of cases when this would happen, and what
        # user expectations are.
        try:
            if items in self.items:
                items = [items]
        except TypeError:
            pass
        x = np.stack([self.norm_vectors[self.items[x]] for x in items])

        result = self._batch(x,
                             batch_size,
                             num+1,
                             show_progressbar,
                             return_names)

        # list call consumes the generator.
        return [x[1:] for x in result]