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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 merge_indexes( indexes, # type: Dict[Any, Union[Any, List[Any]]] variables, # type: Dict[Any, Variable] coord_names, # type: Set append=False, # type: bool ):
 """Merge variables into multi-indexes. Not public API. Used in Dataset and DataArray set_index methods. """

    vars_to_replace = {}  # Dict[Any, Variable]
    vars_to_remove = []  # type: list

    for dim, var_names in indexes.items():
        if isinstance(var_names, str):
            var_names = [var_names]

        names, codes, levels = [], [], []  # type: (list, list, list)
        current_index_variable = variables.get(dim)

        for n in var_names:
            var = variables[n]
            if (current_index_variable is not None and
                    var.dims != current_index_variable.dims):
                raise ValueError(
                    "dimension mismatch between %r %s and %r %s"
                    % (dim, current_index_variable.dims, n, var.dims))

        if current_index_variable is not None and append:
            current_index = current_index_variable.to_index()
            if isinstance(current_index, pd.MultiIndex):
                try:
                    current_codes = current_index.codes
                except AttributeError:
                    # fpr pandas<0.24
                    current_codes = current_index.labels
                names.extend(current_index.names)
                codes.extend(current_codes)
                levels.extend(current_index.levels)
            else:
                names.append('%s_level_0' % dim)
                cat = pd.Categorical(current_index.values, ordered=True)
                codes.append(cat.codes)
                levels.append(cat.categories)

        if not len(names) and len(var_names) == 1:
            idx = pd.Index(variables[var_names[0]].values)

        else:
            for n in var_names:
                names.append(n)
                var = variables[n]
                cat = pd.Categorical(var.values, ordered=True)
                codes.append(cat.codes)
                levels.append(cat.categories)

            idx = pd.MultiIndex(levels, codes, names=names)

        vars_to_replace[dim] = IndexVariable(dim, idx)
        vars_to_remove.extend(var_names)

    new_variables = OrderedDict([(k, v) for k, v in variables.items()
                                 if k not in vars_to_remove])
    new_variables.update(vars_to_replace)
    new_coord_names = coord_names | set(vars_to_replace)
    new_coord_names -= set(vars_to_remove)

    return new_variables, new_coord_names