How to use the mlxtend.externals.six.iteritems function in mlxtend

def get_params(self, deep=True):
        """Return estimator parameter names for GridSearch support."""
        if not deep:
            return super(EnsembleVoteClassifier, self).get_params(deep=False)
        else:
            out = self.named_clfs.copy()
            for name, step in six.iteritems(self.named_clfs):
                for key, value in six.iteritems(step.get_params(deep=True)):
                    out['%s__%s' % (name, key)] = value

            for key, value in six.iteritems(super(EnsembleVoteClassifier,
                                            self).get_params(deep=False)):
                out['%s' % key] = value
            return out

def get_params(self, deep=True):
        """Return estimator parameter names for GridSearch support."""
        if not deep:
            return super(EnsembleVoteClassifier, self).get_params(deep=False)
        else:
            out = self.named_clfs.copy()
            for name, step in six.iteritems(self.named_clfs):
                for key, value in six.iteritems(step.get_params(deep=True)):
                    out['%s__%s' % (name, key)] = value

            for key, value in six.iteritems(super(EnsembleVoteClassifier,
                                            self).get_params(deep=False)):
                out['%s' % key] = value
            return out

def _name_estimators(estimators):
    """Generate names for estimators."""

    names = [type(estimator).__name__.lower() for estimator in estimators]
    namecount = defaultdict(int)
    for _, name in zip(estimators, names):
        namecount[name] += 1

    for k, v in list(six.iteritems(namecount)):
        if v == 1:
            del namecount[k]

    for i in reversed(range(len(estimators))):
        name = names[i]
        if name in namecount:
            names[i] += "-%d" % namecount[name]
            namecount[name] -= 1

    return list(zip(names, estimators))

def get_params(self, deep=True):
        """Return estimator parameter names for GridSearch support."""
        if not deep:
            return super(EnsembleVoteClassifier, self).get_params(deep=False)
        else:
            out = self.named_clfs.copy()
            for name, step in six.iteritems(self.named_clfs):
                for key, value in six.iteritems(step.get_params(deep=True)):
                    out['%s__%s' % (name, key)] = value

            for key, value in six.iteritems(super(EnsembleVoteClassifier,
                                            self).get_params(deep=False)):
                out['%s' % key] = value
            return out