def control_flow_infer(graph: Graph, node_name: str):
"""
Executes constant control flow. Propagates nodes executability
"""
if graph.node[node_name]['kind'] == 'data':
return
def mark_executability(node_id: str, is_executable: bool):
if is_executable and not graph.node[node_id]['executable']:
return
graph.node[node_id]['executable'] = is_executable
in_edges_with_data = graph.in_edges(node_name, data=True)
in_df_edges_with_data = [(u, v, attrs) for u, v, attrs in in_edges_with_data
if 'control_flow_edge' not in attrs or not attrs['control_flow_edge']]
in_cf_edges_with_data = [(u, v, attrs) for u, v, attrs in in_edges_with_data
if 'control_flow_edge' in attrs and attrs['control_flow_edge']]
is_executable_df = all([graph.node[u]['executable'] for u, _, attrs in in_df_edges_with_data]
if len(in_df_edges_with_data) else [True])
is_executable_cf = all([graph.node[u]['executable'] for u, _, attrs in in_cf_edges_with_data]
if len(in_cf_edges_with_data) else [True])
is_executable = is_executable_df and is_executable_cf
node = Node(graph, node_name)
if 'cf_infer' in graph.node[node_name] and callable(node.cf_infer):
node.cf_infer(node, is_executable, mark_executability)
else:
for _, out_data in graph.out_edges(node_name):
mark_executability(out_data, is_executable)
