---
title: tasks
sidebarTitle: tasks
---

# `prefect.tasks`



Module containing the base workflow task class and decorator - for most use cases, using the `@task` decorator is preferred.


## Functions

### `task_input_hash` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L163" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
task_input_hash(context: 'TaskRunContext', arguments: dict[str, Any]) -> Optional[str]
```


A task cache key implementation which hashes all inputs to the task using a JSON or
cloudpickle serializer. If any arguments are not JSON serializable, the pickle
serializer is used as a fallback. If cloudpickle fails, this will return a null key
indicating that a cache key could not be generated for the given inputs.

**Args:**
- `context`: the active `TaskRunContext`
- `arguments`: a dictionary of arguments to be passed to the underlying task

**Returns:**
- a string hash if hashing succeeded, else `None`


### `exponential_backoff` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L189" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
exponential_backoff(backoff_factor: float) -> Callable[[int], list[float]]
```


A task retry backoff utility that configures exponential backoff for task retries.
The exponential backoff design matches the urllib3 implementation.

**Args:**
- `backoff_factor`: the base delay for the first retry, subsequent retries will
increase the delay time by powers of 2.

**Returns:**
- a callable that can be passed to the task constructor


### `task` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L1933" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
task(__fn: Optional[Callable[P, R]] = None)
```


Decorator to designate a function as a task in a Prefect workflow.

This decorator may be used for asynchronous or synchronous functions.

**Args:**
- `name`: An optional name for the task; if not provided, the name will be inferred
from the given function.
- `description`: An optional string description for the task.
- `tags`: An optional set of tags to be associated with runs of this task. These
tags are combined with any tags defined by a `prefect.tags` context at
task runtime.
- `version`: An optional string specifying the version of this task definition
- `cache_key_fn`: An optional callable that, given the task run context and call
parameters, generates a string key; if the key matches a previous completed
state, that state result will be restored instead of running the task again.
- `cache_expiration`: An optional amount of time indicating how long cached states
for this task should be restorable; if not provided, cached states will
never expire.
- `task_run_name`: An optional name to distinguish runs of this task; this name can be provided
as a string template with the task's keyword arguments as variables,
or a function that returns a string.
- `retries`: An optional number of times to retry on task run failure
- `retry_delay_seconds`: Optionally configures how long to wait before retrying the
task after failure. This is only applicable if `retries` is nonzero. This
setting can either be a number of seconds, a list of retry delays, or a
callable that, given the total number of retries, generates a list of retry
delays. If a number of seconds, that delay will be applied to all retries.
If a list, each retry will wait for the corresponding delay before retrying.
When passing a callable or a list, the number of
configured retry delays cannot exceed 50.
- `retry_jitter_factor`: An optional factor that defines the factor to which a
retry can be jittered in order to avoid a "thundering herd".
- `persist_result`: A toggle indicating whether the result of this task
should be persisted to result storage. Defaults to `None`, which
indicates that the global default should be used (which is `True` by
default).
- `result_storage`: An optional block to use to persist the result of this task.
This can be either a saved block instance or a string reference (e.g.,
"local-file-system/my-storage"). Block instances must have `.save()` called
first since decorators execute at import time. String references are resolved
at runtime and recommended for testing scenarios. Defaults to the value set
in the flow the task is called in.
- `result_storage_key`: An optional key to store the result in storage at when persisted.
Defaults to a unique identifier.
- `result_serializer`: An optional serializer to use to serialize the result of this
task for persistence. Defaults to the value set in the flow the task is
called in.
- `timeout_seconds`: An optional number of seconds indicating a maximum runtime for
the task. If the task exceeds this runtime, it will be marked as failed.
- `log_prints`: If set, `print` statements in the task will be redirected to the
Prefect logger for the task run. Defaults to `None`, which indicates
that the value from the flow should be used.
- `refresh_cache`: If set, cached results for the cache key are not used.
Defaults to `None`, which indicates that a cached result from a previous
execution with matching cache key is used.
- `on_failure`: An optional list of callables to run when the task enters a failed state.
- `on_completion`: An optional list of callables to run when the task enters a completed state.
- `retry_condition_fn`: An optional callable run when a task run returns a Failed state. Should
return `True` if the task should continue to its retry policy (e.g. `retries=3`), and `False` if the task
should end as failed. Defaults to `None`, indicating the task should always continue
to its retry policy.
- `viz_return_value`: An optional value to return when the task dependency tree is visualized.
- `asset_deps`: An optional list of upstream assets that this task depends on.

**Returns:**
- A callable `Task` object which, when called, will submit the task for execution.

**Examples:**

Define a simple task

```python
@task
def add(x, y):
    return x + y
```

Define an async task

```python
@task
async def add(x, y):
    return x + y
```

Define a task with tags and a description

```python
@task(tags={"a", "b"}, description="This task is empty but its my first!")
def my_task():
    pass
```

Define a task with a custom name

```python
@task(name="The Ultimate Task")
def my_task():
    pass
```

Define a task that retries 3 times with a 5 second delay between attempts

```python
from random import randint

@task(retries=3, retry_delay_seconds=5)
def my_task():
    x = randint(0, 5)
    if x >= 3:  # Make a task that fails sometimes
        raise ValueError("Retry me please!")
    return x
```

Define a task that is cached for a day based on its inputs

```python
from prefect.tasks import task_input_hash
from datetime import timedelta

@task(cache_key_fn=task_input_hash, cache_expiration=timedelta(days=1))
def my_task():
    return "hello"
```


## Classes

### `TaskRunNameCallbackWithParameters` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L103" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

**Methods:**

#### `is_callback_with_parameters` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L105" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
is_callback_with_parameters(cls, callable: Callable[..., str]) -> TypeIs[Self]
```

### `TaskOptions` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L123" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>


A TypedDict representing all available task configuration options.

This can be used with `Unpack` to provide type hints for **kwargs.


### `Task` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L301" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>


A Prefect task definition.

Wraps a function with an entrypoint to the Prefect engine. Calling this class within a flow function
creates a new task run.

To preserve the input and output types, we use the generic type variables P and R for "Parameters" and
"Returns" respectively.

**Args:**
- `fn`: The function defining the task.
- `name`: An optional name for the task; if not provided, the name will be inferred
from the given function.
- `description`: An optional string description for the task.
- `tags`: An optional set of tags to be associated with runs of this task. These
tags are combined with any tags defined by a `prefect.tags` context at
task runtime.
- `version`: An optional string specifying the version of this task definition
- `cache_policy`: A cache policy that determines the level of caching for this task
- `cache_key_fn`: An optional callable that, given the task run context and call
parameters, generates a string key; if the key matches a previous completed
state, that state result will be restored instead of running the task again.
- `cache_expiration`: An optional amount of time indicating how long cached states
for this task should be restorable; if not provided, cached states will
never expire.
- `task_run_name`: An optional name to distinguish runs of this task; this name can be provided
as a string template with the task's keyword arguments as variables,
or a function that returns a string.
- `retries`: An optional number of times to retry on task run failure.
- `retry_delay_seconds`: Optionally configures how long to wait before retrying the
task after failure. This is only applicable if `retries` is nonzero. This
setting can either be a number of seconds, a list of retry delays, or a
callable that, given the total number of retries, generates a list of retry
delays. If a number of seconds, that delay will be applied to all retries.
If a list, each retry will wait for the corresponding delay before retrying.
When passing a callable or a list, the number of configured retry delays
cannot exceed 50.
- `retry_jitter_factor`: An optional factor that defines the factor to which a retry
can be jittered in order to avoid a "thundering herd".
- `persist_result`: A toggle indicating whether the result of this task
should be persisted to result storage. Defaults to `None`, which
indicates that the global default should be used (which is `True` by
default).
- `result_storage`: An optional block to use to persist the result of this task.
This can be either a saved block instance or a string reference (e.g.,
"local-file-system/my-storage"). Block instances must have `.save()` called
first since decorators execute at import time. String references are resolved
at runtime and recommended for testing scenarios. Defaults to the value set
in the flow the task is called in.
- `result_storage_key`: An optional key to store the result in storage at when persisted.
Defaults to a unique identifier.
- `result_serializer`: An optional serializer to use to serialize the result of this
task for persistence. Defaults to the value set in the flow the task is
called in.
- `timeout_seconds`: An optional number of seconds indicating a maximum runtime for
the task. If the task exceeds this runtime, it will be marked as failed.
- `log_prints`: If set, `print` statements in the task will be redirected to the
Prefect logger for the task run. Defaults to `None`, which indicates
that the value from the flow should be used.
- `refresh_cache`: If set, cached results for the cache key are not used.
Defaults to `None`, which indicates that a cached result from a previous
execution with matching cache key is used.
- `on_failure`: An optional list of callables to run when the task enters a failed state.
- `on_completion`: An optional list of callables to run when the task enters a completed state.
- `on_commit`: An optional list of callables to run when the task's idempotency record is committed.
- `on_rollback`: An optional list of callables to run when the task rolls back.
- `retry_condition_fn`: An optional callable run when a task run returns a Failed state. Should
return `True` if the task should continue to its retry policy (e.g. `retries=3`), and `False` if the task
should end as failed. Defaults to `None`, indicating the task should always continue
to its retry policy.
- `viz_return_value`: An optional value to return when the task dependency tree is visualized.
- `asset_deps`: An optional list of upstream assets that this task depends on.


**Methods:**

#### `apply_async` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L1630" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
apply_async(self, args: Optional[tuple[Any, ...]] = None, kwargs: Optional[dict[str, Any]] = None, wait_for: Optional[Iterable[PrefectFuture[R]]] = None, dependencies: Optional[dict[str, set[RunInput]]] = None) -> PrefectDistributedFuture[R]
```

Create a pending task run for a task worker to execute.

**Args:**
- `args`: Arguments to run the task with
- `kwargs`: Keyword arguments to run the task with

**Returns:**
- A PrefectDistributedFuture object representing the pending task run

Examples:

    Define a task

    ```python
    from prefect import task
    @task
    def my_task(name: str = "world"):
        return f"hello {name}"
    ```

    Create a pending task run for the task

    ```python
    from prefect import flow
    @flow
    def my_flow():
        my_task.apply_async(("marvin",))
    ```

    Wait for a task to finish

    ```python
    @flow
    def my_flow():
        my_task.apply_async(("marvin",)).wait()
    ```

    ```python
    @flow
    def my_flow():
        print(my_task.apply_async(("marvin",)).result())

    my_flow()
    # hello marvin
    ```

    TODO\: Enforce ordering between tasks that do not exchange data
    ```python
    @task
    def task_1():
        pass

    @task
    def task_2():
        pass

    @flow
    def my_flow():
        x = task_1.apply_async()

        # task 2 will wait for task_1 to complete
        y = task_2.apply_async(wait_for=[x])
    ```


#### `create_local_run` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L965" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
create_local_run(self, client: Optional['PrefectClient'] = None, id: Optional[UUID] = None, parameters: Optional[dict[str, Any]] = None, flow_run_context: Optional[FlowRunContext] = None, parent_task_run_context: Optional[TaskRunContext] = None, wait_for: Optional[OneOrManyFutureOrResult[Any]] = None, extra_task_inputs: Optional[dict[str, set[RunInput]]] = None, deferred: bool = False) -> TaskRun
```

#### `create_run` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L862" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
create_run(self, client: Optional['PrefectClient'] = None, id: Optional[UUID] = None, parameters: Optional[dict[str, Any]] = None, flow_run_context: Optional[FlowRunContext] = None, parent_task_run_context: Optional[TaskRunContext] = None, wait_for: Optional[OneOrManyFutureOrResult[Any]] = None, extra_task_inputs: Optional[dict[str, set[RunInput]]] = None, deferred: bool = False) -> TaskRun
```

#### `delay` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L1744" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
delay(self, *args: P.args, **kwargs: P.kwargs) -> PrefectDistributedFuture[R]
```

An alias for `apply_async` with simpler calling semantics.

Avoids having to use explicit "args" and "kwargs" arguments. Arguments
will pass through as-is to the task.

Examples:

        Define a task

        ```python
        from prefect import task
        @task
        def my_task(name: str = "world"):
            return f"hello {name}"
        ```

        Create a pending task run for the task

        ```python
        from prefect import flow
        @flow
        def my_flow():
            my_task.delay("marvin")
        ```

        Wait for a task to finish

        ```python
        @flow
        def my_flow():
            my_task.delay("marvin").wait()
        ```

        Use the result from a task in a flow

        ```python
        @flow
        def my_flow():
            print(my_task.delay("marvin").result())

        my_flow()
        # hello marvin
        ```


#### `isclassmethod` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L627" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
isclassmethod(self) -> bool
```

#### `ismethod` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L623" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
ismethod(self) -> bool
```

#### `isstaticmethod` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L631" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
isstaticmethod(self) -> bool
```

#### `map` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L1390" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
map(self: 'Task[P, R]', *args: Any, **kwargs: Any) -> list[State[R]]
```

#### `map` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L1400" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
map(self: 'Task[P, R]', *args: Any, **kwargs: Any) -> PrefectFutureList[R]
```

#### `map` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L1409" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
map(self: 'Task[P, R]', *args: Any, **kwargs: Any) -> list[State[R]]
```

#### `map` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L1419" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
map(self: 'Task[P, R]', *args: Any, **kwargs: Any) -> PrefectFutureList[R]
```

#### `map` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L1428" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
map(self: 'Task[P, Coroutine[Any, Any, R]]', *args: Any, **kwargs: Any) -> list[State[R]]
```

#### `map` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L1438" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
map(self: 'Task[P, Coroutine[Any, Any, R]]', *args: Any, **kwargs: Any) -> PrefectFutureList[R]
```

#### `map` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L1447" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
map(self, *args: Any, **kwargs: Any) -> Union[list[State[R]], PrefectFutureList[R]]
```

Submit a mapped run of the task to a worker.

Must be called within a flow run context. Will return a list of futures
that should be waited on before exiting the flow context to ensure all
mapped tasks have completed.

Must be called with at least one iterable and all iterables must be
the same length. Any arguments that are not iterable will be treated as
a static value and each task run will receive the same value.

Will create as many task runs as the length of the iterable(s) in the
backing API and submit the task runs to the flow's task runner. This
call blocks if given a future as input while the future is resolved. It
also blocks while the tasks are being submitted, once they are
submitted, the flow function will continue executing.

This method is always synchronous, even if the underlying user function is asynchronous.

**Args:**
- `*args`: Iterable and static arguments to run the tasks with
- `return_state`: Return a list of Prefect States that wrap the results
of each task run.
- `wait_for`: Upstream task futures to wait for before starting the
task
- `**kwargs`: Keyword iterable arguments to run the task with

**Returns:**
- A list of futures allowing asynchronous access to the state of the
- tasks

Examples:

    Define a task

    ```python
    from prefect import task
    @task
    def my_task(x):
        return x + 1
    ```

    Create mapped tasks

    ```python
    from prefect import flow
    @flow
    def my_flow():
        return my_task.map([1, 2, 3])
    ```

    Wait for all mapped tasks to finish

    ```python
    @flow
    def my_flow():
        futures = my_task.map([1, 2, 3])
        futures.wait():
        # Now all of the mapped tasks have finished
        my_task(10)
    ```

    Use the result from mapped tasks in a flow

    ```python
    @flow
    def my_flow():
        futures = my_task.map([1, 2, 3])
        for x in futures.result():
            print(x)
    my_flow()
    # 2
    # 3
    # 4
    ```

    Enforce ordering between tasks that do not exchange data

    ```python
    @task
    def task_1(x):
        pass

    @task
    def task_2(y):
        pass

    @flow
    def my_flow():
        x = task_1.submit()

        # task 2 will wait for task_1 to complete
        y = task_2.map([1, 2, 3], wait_for=[x])
        return y
    ```

    Use a non-iterable input as a constant across mapped tasks

    ```python
    @task
    def display(prefix, item):
       print(prefix, item)

    @flow
    def my_flow():
        return display.map("Check it out: ", [1, 2, 3])

    my_flow()
    # Check it out: 1
    # Check it out: 2
    # Check it out: 3
    ```

    Use `unmapped` to treat an iterable argument as a constant

    ```python
    from prefect import unmapped

    @task
    def add_n_to_items(items, n):
        return [item + n for item in items]

    @flow
    def my_flow():
        return add_n_to_items.map(unmapped([10, 20]), n=[1, 2, 3])

    my_flow()
    # [[11, 21], [12, 22], [13, 23]]
    ```


#### `on_commit` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L850" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
on_commit(self, fn: Callable[['Transaction'], None]) -> Callable[['Transaction'], None]
```

#### `on_completion` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L838" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
on_completion(self, fn: StateHookCallable) -> StateHookCallable
```

#### `on_failure` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L842" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
on_failure(self, fn: StateHookCallable) -> StateHookCallable
```

#### `on_rollback` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L856" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
on_rollback(self, fn: Callable[['Transaction'], None]) -> Callable[['Transaction'], None]
```

#### `on_running` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L846" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
on_running(self, fn: StateHookCallable) -> StateHookCallable
```

#### `serve` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L1793" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
serve(self) -> NoReturn
```

Serve the task using the provided task runner. This method is used to
establish a websocket connection with the Prefect server and listen for
submitted task runs to execute.

**Args:**
- `task_runner`: The task runner to use for serving the task. If not provided,
the default task runner will be used.

**Examples:**

Serve a task using the default task runner
```python
@task
def my_task():
    return 1

my_task.serve()
```


#### `submit` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L1208" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
submit(self: 'Task[P, R]', *args: P.args, **kwargs: P.kwargs) -> PrefectFuture[R]
```

#### `submit` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L1215" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
submit(self: 'Task[P, Coroutine[Any, Any, R]]', *args: P.args, **kwargs: P.kwargs) -> PrefectFuture[R]
```

#### `submit` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L1224" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
submit(self: 'Task[P, R]', *args: P.args, **kwargs: P.kwargs) -> PrefectFuture[R]
```

#### `submit` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L1233" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
submit(self: 'Task[P, Coroutine[Any, Any, R]]', *args: P.args, **kwargs: P.kwargs) -> State[R]
```

#### `submit` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L1242" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
submit(self: 'Task[P, R]', *args: P.args, **kwargs: P.kwargs) -> State[R]
```

#### `submit` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L1250" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
submit(self: 'Union[Task[P, R], Task[P, Coroutine[Any, Any, R]]]', *args: Any, **kwargs: Any)
```

Submit a run of the task to the engine.

Will create a new task run in the backing API and submit the task to the flow's
task runner. This call only blocks execution while the task is being submitted,
once it is submitted, the flow function will continue executing.

This method is always synchronous, even if the underlying user function is asynchronous.

**Args:**
- `*args`: Arguments to run the task with
- `return_state`: Return the result of the flow run wrapped in a
Prefect State.
- `wait_for`: Upstream task futures to wait for before starting the task
- `**kwargs`: Keyword arguments to run the task with

**Returns:**
- If `return_state` is False a future allowing asynchronous access to
the state of the task
- If `return_state` is True a future wrapped in a Prefect State allowing asynchronous access to
the state of the task

Examples:

    Define a task

    ```python
    from prefect import task
    @task
    def my_task():
        return "hello"
    ```

    Run a task in a flow

    ```python
    from prefect import flow
    @flow
    def my_flow():
        my_task.submit()
    ```

    Wait for a task to finish

    ```python
    @flow
    def my_flow():
        my_task.submit().wait()
    ```

    Use the result from a task in a flow

    ```python
    @flow
    def my_flow():
        print(my_task.submit().result())

    my_flow()
    # hello
    ```

    Run an async task in an async flow

    ```python
    @task
    async def my_async_task():
        pass

    @flow
    async def my_flow():
        my_async_task.submit()
    ```

    Run a sync task in an async flow

    ```python
    @flow
    async def my_flow():
        my_task.submit()
    ```

    Enforce ordering between tasks that do not exchange data

    ```python
    @task
    def task_1():
        pass

    @task
    def task_2():
        pass

    @flow
    def my_flow():
        x = task_1.submit()

        # task 2 will wait for task_1 to complete
        y = task_2.submit(wait_for=[x])
    ```


#### `with_options` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L655" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
with_options(self) -> 'Task[P, R]'
```

Create a new task from the current object, updating provided options.

**Args:**
- `name`: A new name for the task.
- `description`: A new description for the task.
- `tags`: A new set of tags for the task. If given, existing tags are ignored,
not merged.
- `cache_key_fn`: A new cache key function for the task.
- `cache_expiration`: A new cache expiration time for the task.
- `task_run_name`: An optional name to distinguish runs of this task; this name can be provided
as a string template with the task's keyword arguments as variables,
or a function that returns a string.
- `retries`: A new number of times to retry on task run failure.
- `retry_delay_seconds`: Optionally configures how long to wait before retrying
the task after failure. This is only applicable if `retries` is nonzero.
This setting can either be a number of seconds, a list of retry delays,
or a callable that, given the total number of retries, generates a list
of retry delays. If a number of seconds, that delay will be applied to
all retries. If a list, each retry will wait for the corresponding delay
before retrying. When passing a callable or a list, the number of
configured retry delays cannot exceed 50.
- `retry_jitter_factor`: An optional factor that defines the factor to which a
retry can be jittered in order to avoid a "thundering herd".
- `persist_result`: A new option for enabling or disabling result persistence.
- `result_storage`: A new storage type to use for results.
- `result_serializer`: A new serializer to use for results.
- `result_storage_key`: A new key for the persisted result to be stored at.
- `timeout_seconds`: A new maximum time for the task to complete in seconds.
- `log_prints`: A new option for enabling or disabling redirection of `print` statements.
- `refresh_cache`: A new option for enabling or disabling cache refresh.
- `on_completion`: A new list of callables to run when the task enters a completed state.
- `on_failure`: A new list of callables to run when the task enters a failed state.
- `retry_condition_fn`: An optional callable run when a task run returns a Failed state.
Should return `True` if the task should continue to its retry policy, and `False`
if the task should end as failed. Defaults to `None`, indicating the task should
always continue to its retry policy.
- `viz_return_value`: An optional value to return when the task dependency tree is visualized.

**Returns:**
- A new `Task` instance.

Examples:

    Create a new task from an existing task and update the name:

    ```python
    @task(name="My task")
    def my_task():
        return 1

    new_task = my_task.with_options(name="My new task")
    ```

    Create a new task from an existing task and update the retry settings:

    ```python
    from random import randint

    @task(retries=1, retry_delay_seconds=5)
    def my_task():
        x = randint(0, 5)
        if x >= 3:  # Make a task that fails sometimes
            raise ValueError("Retry me please!")
        return x

    new_task = my_task.with_options(retries=5, retry_delay_seconds=2)
    ```

    Use a task with updated options within a flow:

    ```python
    @task(name="My task")
    def my_task():
        return 1

    @flow
    my_flow():
        new_task = my_task.with_options(name="My new task")
        new_task()
    ```


### `MaterializingTask` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L2155" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>


A task that materializes Assets.

**Args:**
- `assets`: List of Assets that this task materializes (can be str or Asset)
- `materialized_by`: An optional tool that materialized the asset e.g. "dbt" or "spark"
- `**task_kwargs`: All other Task arguments


**Methods:**

#### `with_options` <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/prefect/tasks.py#L2180" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

```python
with_options(self, assets: Optional[Sequence[Union[str, Asset]]] = None, **task_kwargs: Unpack[TaskOptions]) -> 'MaterializingTask[P, R]'
```
