### 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)

"""Test that default_args are captured when calling the function correctly"""
        @task_decorator
        def do_run():
            return 4
        self.dag.default_args["owner"] = "airflow"
        with self.dag:
            ret = do_run()
        assert ret.operator.owner == "airflow"
        @task_decorator
        def test_apply_default_raise(unknown):
            return unknown
        with pytest.raises(TypeError):
            with self.dag:
                test_apply_default_raise()
        @task_decorator
        def test_apply_default(owner):