### Apache Airflow version

2.5.1

### What happened

When using the TaskFlow API, I like to generally keep a good practice of adding type annotations in the TaskFlow functions so others reading the DAG and task code have better context around inputs/outputs, keep imports solely used for typing behind `typing.TYPE_CHECKING`, and utilize PEP 563 for forwarding annotation evaluations. Unfortunately, when using ~PEP 563 _and_ `TYPE_CHECKING`~ just TYPE_CHECKING, DAG import errors occur with a "NameError: <name> is not defined." exception.

### What you think should happen instead

Users should be free to use ~PEP 563 and~ `TYPE_CHECKING` when using the TaskFlow API and not hit DAG import errors along the way.

### How to reproduce

Using a straightforward use case of transforming a DataFrame, let's assume this toy example:

```py
from __future__ import annotations

from typing import TYPE_CHECKING, Any

from pendulum import datetime

from airflow.decorators import dag, task

if TYPE_CHECKING:
    from pandas import DataFrame


@dag(start_date=datetime(2023, 1, 1), schedule=None)
def multiple_outputs():
    @task()
    def transform(df: DataFrame) -> dict[str, Any]:
        ...

    transform()

multiple_outputs()
```
Add this DAG to your DAGS_FOLDER and the following import error should be observed:
<img width="641" alt="image" src="https://user-images.githubusercontent.com/48934154/217713685-ec29d5cc-4a48-4049-8dfa-56cbd76cddc3.png">


### Operating System

Debian GNU/Linux

### Versions of Apache Airflow Providers

apache-airflow-providers-amazon==6.2.0
apache-airflow-providers-apache-hive==5.1.1
apache-airflow-providers-apache-livy==3.2.0
apache-airflow-providers-celery==3.1.0
apache-airflow-providers-cncf-kubernetes==5.1.1
apache-airflow-providers-common-sql==1.3.3
apache-airflow-providers-databricks==4.0.0
apache-airflow-providers-dbt-cloud==2.3.1
apache-airflow-providers-elasticsearch==4.3.3
apache-airflow-providers-ftp==3.3.0
apache-airflow-providers-google==8.8.0
apache-airflow-providers-http==4.1.1
apache-airflow-providers-imap==3.1.1
apache-airflow-providers-microsoft-azure==5.1.0
apache-airflow-providers-postgres==5.4.0
apache-airflow-providers-redis==3.1.0
apache-airflow-providers-sftp==4.2.1
apache-airflow-providers-snowflake==4.0.2
apache-airflow-providers-sqlite==3.3.1
apache-airflow-providers-ssh==3.4.0
astronomer-providers==1.14.0

### Deployment

Astronomer

### Deployment details

OOTB local Airflow install with LocalExecutor built with the Astro CLI.

### Anything else

- This behavior/error was not observed using Airflow 2.4.3. 
- As a workaround, `multiple_outputs` can be explicitly set on the TaskFlow function to skip the inferral.

### Are you willing to submit PR?

- [X] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

:param multiple_outputs: If set to True, the decorated function's return
        value will be unrolled to multiple XCom values. Dict will unroll to XCom
        values with its keys as XCom keys. If set to False (default), only at
        most one XCom value is pushed.
    :param decorated_operator_class: The operator that executes the logic needed
        to run the python function in the correct environment.
    Other kwargs are directly forwarded to the underlying operator class when
    it's instantiated.
    """
    if multiple_outputs is None:
        multiple_outputs = cast(bool, attr.NOTHING)
    if python_callable:
        decorator = _TaskDecorator(
            function=python_callable,
            multiple_outputs=multiple_outputs,
            operator_class=decorated_operator_class,
            kwargs=kwargs,
        )
        return cast(TaskDecorator, decorator)
    elif python_callable is not None:
        raise TypeError("No args allowed while using @task, use kwargs instead")
    def decorator_factory(python_callable):
        return _TaskDecorator(
            function=python_callable,
            multiple_outputs=multiple_outputs,
            operator_class=decorated_operator_class,
            kwargs=kwargs,
        )
    return cast(TaskDecorator, decorator_factory)