&`iI xddlZddlZddlmZddlmZmZddlmZ ddiZ eeeddd d he zZGd d Z dS) N)0COPY_DIRECTORY_CHECKPOINTS_INSTEAD_OF_MOVING_ENV)$ENABLE_V2_MIGRATION_WARNINGS_ENV_VARRAY_CHDIR_TO_TRIAL_DIR)ENV_VARS_TO_PROPAGATEPL_DISABLE_FORK1AWS_ACCESS_KEY_IDAWS_SECRET_ACCESS_KEYAWS_SECURITY_TOKENAWS_SESSION_TOKENceZdZdZdZdZdS)_ActorClassCacheu Caches actor classes. ray.remote is a registration call. It sends the serialized object to the key value store (redis), and will be fetched at an arbitrary worker later. Registration does not use any Ray scheduling resources. Later, class.remote() actually creates the remote actor. The actor will be instantiated on some arbitrary machine, according to the underlying Ray scheduler. Without this cache, you would register the same serialized object over and over again. Naturally, since redis doesn’t spill to disk, this can easily nuke the redis instance (and basically blow up Ray). This cache instead allows us to register once and only once. Note that we assume there can be multiple trainables in the system at once. ci|_dS)N)_cache)selfs r/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/ray/tune/execution/class_cache.py__init__z_ActorClassCache.__init__5s  ct}tD]%}|tjvrtj|||<&d|i}||jvr(t j||}||j|<|j|S)z;Gets the wrapped trainable_cls, otherwise calls ray.remote.env_vars) runtime_env)DEFAULT_ENV_VARScopyrosenvironrrayremote)r trainable_clsrenv_var_to_propagater remote_clss rgetz_ActorClassCache.get8s#((**$9 R R #rz1113r+s NNNNNN s 9," " #*#*#*#*#*#*#*#*#*#*r