def compute_live_variables(cfg, blocks, var_def_map, var_dead_map):
    """
    Compute the live variables at the beginning of each block
    and at each yield point.
    The ``var_def_map`` and ``var_dead_map`` indicates the variable defined
    and deleted at each block, respectively.
    """
    # live var at the entry per block
    block_entry_vars = defaultdict(set)

    def fix_point_progress():
        return tuple(map(len, block_entry_vars.values()))

    old_point = None
    new_point = fix_point_progress()

    # Propagate defined variables and still live the successors.
    # (note the entry block automatically gets an empty set)

    # Note: This is finding the actual available variables at the entry
    #       of each block. The algorithm in compute_live_map() is finding
    #       the variable that must be available at the entry of each block.
    #       This is top-down in the dataflow.  The other one is bottom-up.
    while old_point != new_point:
        # We iterate until the result stabilizes.  This is necessary
        # because of loops in the graphself.
        for offset in blocks:
            # vars available + variable defined
            avail = block_entry_vars[offset] | var_def_map[offset]
            # subtract variables deleted
            avail -= var_dead_map[offset]
            # add ``avail`` to each successors
            for succ, _data in cfg.successors(offset):
                block_entry_vars[succ] |= avail

        old_point = new_point
        new_point = fix_point_progress()

    return block_entry_vars

    Compute the live variables at the beginning of each block
    and at each yield point.
    The ``var_def_map`` and ``var_dead_map`` indicates the variable defined
    and deleted at each block, respectively.
    
numba/core/analysis.py