> ## Documentation Index > Fetch the complete documentation index at: https://docs.prefect.io/llms.txt > Use this file to discover all available pages before exploring further. # engine # `prefect.utilities.engine` ## Functions ### `is_prefect_sigterm_handler_installed` ```python theme={null} is_prefect_sigterm_handler_installed() -> bool ``` Return whether Prefect's SIGTERM bridge is currently installed. ### `can_ack_control_intent` ```python theme={null} can_ack_control_intent() -> bool ``` Return whether the child can safely acknowledge a queued control intent. The check is protected by the same lock used when `capture_sigterm()` installs and restores Prefect's SIGTERM bridge. On POSIX, the runner's subsequent real `SIGTERM` is the actual cancellation trigger, so the child only needs to verify that Prefect still owns the live bridge before advertising readiness with `b"a"`. ### `commit_control_intent_and_ack` ```python theme={null} commit_control_intent_and_ack(commit_intent: Callable[[], None], clear_intent: Callable[[], None], send_ack: Callable[[], None], trigger_cancel: Callable[[], None] | None = None) -> bool ``` Atomically commit control intent and acknowledge it to the runner. The SIGTERM bridge check, intent commit, and ack write must share the same lock used by `capture_sigterm()` to install and restore Prefect's SIGTERM handler. Otherwise, teardown can restore the original handler after the child decides it is safe to ack but before the runner observes `b"a"`. ### `collect_task_run_inputs` ```python theme={null} collect_task_run_inputs(expr: Any, max_depth: int = -1) -> set[Union[TaskRunResult, FlowRunResult]] ``` This function recurses through an expression to generate a set of any discernible task run inputs it finds in the data structure. It produces a set of all inputs found. Examples: ```python theme={null} task_inputs = { k: await collect_task_run_inputs(v) for k, v in parameters.items() } ``` ### `collect_task_run_inputs_sync` ```python theme={null} collect_task_run_inputs_sync(expr: Any, future_cls: Any = PrefectFuture, max_depth: int = -1) -> set[Union[TaskRunResult, FlowRunResult]] ``` This function recurses through an expression to generate a set of any discernible task run inputs it finds in the data structure. It produces a set of all inputs found. **Examples:** ```python theme={null} task_inputs = { k: collect_task_run_inputs_sync(v) for k, v in parameters.items() } ``` ### `capture_sigterm` ```python theme={null} capture_sigterm() -> Generator[None, Any, None] ``` Install a SIGTERM handler that raises `TerminationSignal`. Only the outermost Prefect flow engine in a process installs the handler by default. Nested subflow engines reuse that existing Prefect-owned handler when it is still active; if user or library code temporarily replaced `SIGTERM`, the nested scope reinstalls Prefect's bridge for the duration of that scope. This guard is based on explicit local ownership state plus the currently installed handler, not on `FlowRunContext`: a fresh subprocess may hydrate a parent flow context before its own engine starts, and still needs to install a SIGTERM bridge for the child process. The handler does not need to interpret intent. The engine's `except TerminationSignal` block consults `prefect._internal.control_listener.get_intent()` directly when dispatching (today: `handle_cancellation` vs `handle_crash`; in a future PR: plus `handle_suspension`). The runner control listener only connects while this context is active. Cancels that land before the bridge is armed fall back to the runner's existing crash-style termination path; once this context is active, the child can acknowledge control intent and treat the later SIGTERM as an intentional cancellation. ### `resolve_inputs` ```python theme={null} resolve_inputs(parameters: dict[str, Any], return_data: bool = True, max_depth: int = -1) -> dict[str, Any] ``` Resolve any `Quote`, `PrefectFuture`, or `State` types nested in parameters into data. **Returns:** * A copy of the parameters with resolved data **Raises:** * `UpstreamTaskError`: If any of the upstream states are not `COMPLETED` ### `propose_state` ```python theme={null} propose_state(client: 'PrefectClient', state: State[Any], flow_run_id: UUID, force: bool = False) -> State[Any] ``` Propose a new state for a flow run, invoking Prefect orchestration logic. If the proposed state is accepted, the provided `state` will be augmented with details and returned. If the proposed state is rejected, a new state returned by the Prefect API will be returned. If the proposed state results in a WAIT instruction from the Prefect API, the function will sleep and attempt to propose the state again. If the proposed state results in an ABORT instruction from the Prefect API, an error will be raised. **Args:** * `state`: a new state for a flow run * `flow_run_id`: an optional flow run id, used when proposing flow run states **Returns:** * a State model representation of the flow run state **Raises:** * `prefect.exceptions.Abort`: if an ABORT instruction is received from the Prefect API ### `propose_state_sync` ```python theme={null} propose_state_sync(client: 'SyncPrefectClient', state: State[Any], flow_run_id: UUID, force: bool = False) -> State[Any] ``` Propose a new state for a flow run, invoking Prefect orchestration logic. If the proposed state is accepted, the provided `state` will be augmented with details and returned. If the proposed state is rejected, a new state returned by the Prefect API will be returned. If the proposed state results in a WAIT instruction from the Prefect API, the function will sleep and attempt to propose the state again. If the proposed state results in an ABORT instruction from the Prefect API, an error will be raised. **Args:** * `state`: a new state for the flow run * `flow_run_id`: an optional flow run id, used when proposing flow run states **Returns:** * a State model representation of the flow run state **Raises:** * `ValueError`: if flow\_run\_id is not provided * `prefect.exceptions.Abort`: if an ABORT instruction is received from the Prefect API ### `get_state_for_result` ```python theme={null} get_state_for_result(obj: Any) -> Optional[tuple[State, RunType]] ``` Get the state related to a result object. `link_state_to_result` must have been called first. For objects that support `__weakref__`, the entry stored by `link_state_to_result` carries a weak reference back to the original object. We verify here that the entry's weak reference still points to the *same* object that registered the entry — not just to *some* object that happens to share its `id()`. This prevents stale hits caused by CPython recycling a freed memory address. Stale entries are evicted on detection. For objects that do not support `__weakref__` (plain `dict`, `list`, `set`, `str`, `int`, `tuple`, ...), the entry has no weak reference and we fall back to the legacy `id()`-only lookup. This preserves today's behavior for those types — including the latent stale-id bug — and isolates the limitation to a single named code path. ### `link_state_to_flow_run_result` ```python theme={null} link_state_to_flow_run_result(state: State, result: Any) -> None ``` Creates a link between a state and flow run result ### `link_state_to_task_run_result` ```python theme={null} link_state_to_task_run_result(state: State, result: Any) -> None ``` Creates a link between a state and task run result ### `link_state_to_result` ```python theme={null} link_state_to_result(state: State, result: Any, run_type: RunType) -> None ``` Caches a link between a state and a result and its components using the `id` of the components to map to the state. The cache is persisted to the current flow run context since task relationships are limited to within a flow run. This allows dependency tracking to occur when results are passed around. Note: Because `id` is used, we cannot cache links between singleton objects. We only cache the relationship between components 1-layer deep. Example: Given the result \[1, \["a","b"], ("c",)], the following elements will be mapped to the state: * \[1, \["a","b"], ("c",)] * \["a","b"] * ("c",) Note: the int `1` will not be mapped to the state because it is a singleton. Other Notes: We do not hash the result because: * If changes are made to the object in the flow between task calls, we can still track that they are related. * Hashing can be expensive. * Not all objects are hashable. * Hash-based keying would also conflate equal-but-distinct objects from unrelated tasks. We do not set an attribute, e.g. `__prefect_state__`, on the result because: * Mutating user's objects is dangerous. * Unrelated equality comparisons can break unexpectedly. * The field can be preserved on copy. * We cannot set this attribute on Python built-ins. ### `should_log_prints` ```python theme={null} should_log_prints(flow_or_task: Union['Flow[..., Any]', 'Task[..., Any]']) -> bool ``` ### `check_api_reachable` ```python theme={null} check_api_reachable(client: 'PrefectClient', fail_message: str) -> None ``` ### `emit_task_run_state_change_event` ```python theme={null} emit_task_run_state_change_event(task_run: TaskRun, initial_state: Optional[State[Any]], validated_state: State[Any], follows: Optional[Event] = None) -> Optional[Event] ``` ### `resolve_to_final_result` ```python theme={null} resolve_to_final_result(expr: Any, context: dict[str, Any]) -> Any ``` Resolve any `PrefectFuture`, or `State` types nested in parameters into data. Designed to be use with `visit_collection`. ### `resolve_inputs_sync` ```python theme={null} resolve_inputs_sync(parameters: dict[str, Any], return_data: bool = True, max_depth: int = -1) -> dict[str, Any] ``` Resolve any `Quote`, `PrefectFuture`, or `State` types nested in parameters into data. **Returns:** * A copy of the parameters with resolved data **Raises:** * `UpstreamTaskError`: If any of the upstream states are not `COMPLETED`