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How to use shrink_pass_choice_trees method in hypothesis

Best Python code snippet using hypothesis

shrinker.py

Source:shrinker.py

...445            ]446            + [dfa_replacement(n) for n in SHRINKING_DFAS]447        )448    @derived_value449    def shrink_pass_choice_trees(self):450        return defaultdict(ChoiceTree)451    def fixate_shrink_passes(self, passes):452        """Run steps from each pass in ``passes`` until the current shrink target453        is a fixed point of all of them."""454        passes = list(map(self.shrink_pass, passes))455        any_ran = True456        while any_ran:457            any_ran = False458            reordering = {}459            # We run remove_discarded after every pass to do cleanup460            # keeping track of whether that actually works. Either there is461            # no discarded data and it is basically free, or it reliably works462            # and deletes data, or it doesn't work. In that latter case we turn463            # it off for the rest of this loop through the passes, but will...

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