### 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)

#
#
#
import sys
from collections import namedtuple
from datetime import date, timedelta
from typing import Dict, Tuple  
import pytest
from airflow.decorators import task as task_decorator
from airflow.decorators.base import DecoratedMappedOperator
from airflow.exceptions import AirflowException
from airflow.models import DAG
from airflow.models.baseoperator import BaseOperator
from airflow.models.expandinput import DictOfListsExpandInput
from airflow.models.mappedoperator import MappedOperator
from airflow.models.taskinstance import TaskInstance
from airflow.models.taskmap import TaskMap
from airflow.models.xcom import XCOM_RETURN_KEY
from airflow.models.xcom_arg import PlainXComArg, XComArg
from airflow.utils import timezone
from airflow.utils.state import State
from airflow.utils.task_group import TaskGroup
from airflow.utils.types import DagRunType
from tests.operators.test_python import BasePythonTest
DEFAULT_DATE = timezone.datetime(2016, 1, 1)
class TestAirflowTaskDecorator(BasePythonTest):
    default_date = DEFAULT_DATE
    def test_python_operator_python_callable_is_callable(self):