def METHOD_NAME(file, filters):
    _envs = envs.load_yaml(file)
    all_flattens = {}
    def fatten_env_namespace(namespace_nests, local_envs):
        for k, v in local_envs.items():
            if isinstance(v, dict):
                nests = copy.deepcopy(namespace_nests)
                nests.append(k)
                fatten_env_namespace(nests, v)
            elif (k == "dataset" or k == "phase" or
                  k == "runner") and isinstance(v, list):
                for i in v:
                    if i.get("name") is None:
                        raise ValueError("name must be in dataset list. ", v)
                    nests = copy.deepcopy(namespace_nests)
                    nests.append(k)
                    nests.append(i["name"])
                    fatten_env_namespace(nests, i)
            else:
                global_k = ".".join(namespace_nests + [k])
                all_flattens[global_k] = v
    fatten_env_namespace([], _envs)
    ret = {}
    for k, v in all_flattens.items():
        for f in filters:
            if k.startswith(f):
                ret[k] = v
    return ret