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**Apache Airflow version**: 932f8c2e9360de6371031d4d71df00867a2776e6


**Kubernetes version (if you are using kubernetes)** (use `kubectl version`):

**Environment**: locally run `airflow server`

- **Cloud provider or hardware configuration**:
- **OS** (e.g. from /etc/os-release): mac
- **Kernel** (e.g. `uname -a`):
- **Install tools**:
- **Others**:

**What happened**:

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Task doc is shown on Airflow v1 Task Instance Detail view but not shown on v2.

**What you expected to happen**:

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Task doc is shown.

**How to reproduce it**:

- install airflow latest master
- `airflow server`
- open `tutorial_etl_dag` in `example_dags`
- run dag(I don't know why but task instance detail can't open with error if no dag runs) and open task instance detail

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If you are using kubernetes, please attempt to recreate the issue using minikube or kind.

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- Kind https://kind.sigs.k8s.io/docs/user/quick-start/

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airflow/models/baseoperator.py
:param max_retry_delay: maximum delay interval between retries
    :type max_retry_delay: datetime.timedelta
    :param start_date: The ``start_date`` for the task, determines
        the ``execution_date`` for the first task instance. The best practice
        is to have the start_date rounded
        to your DAG's ``schedule_interval``. Daily jobs have their start_date
        some day at 00:00:00, hourly jobs have their start_date at 00:00
        of a specific hour. Note that Airflow simply looks at the latest
        ``execution_date`` and adds the ``schedule_interval`` to determine
        the next ``execution_date``. It is also very important
        to note that different tasks' dependencies
        need to line up in time. If task A depends on task B and their
        start_date are offset in a way that their execution_date don't line
        up, A's dependencies will never be met. If you are looking to delay
        a task, for example running a daily task at 2AM, look into the
        ``TimeSensor`` and ``TimeDeltaSensor``. We advise against using
        dynamic ``start_date`` and recommend using fixed ones. Read the
        FAQ entry about start_date for more information.
    :type start_date: datetime.datetime
    :param end_date: if specified, the scheduler won't go beyond this date
    :type end_date: datetime.datetime
    :param depends_on_past: when set to true, task instances will run
        sequentially and only if the previous instance has succeeded or has been skipped.
        The task instance for the start_date is allowed to run.
    :type depends_on_past: bool
    :param wait_for_downstream: when set to true, an instance of task
        X will wait for tasks immediately downstream of the previous instance
        of task X to finish successfully or be skipped before it runs. This is useful if the
        different instances of a task X alter the same asset, and this asset
        is used by tasks downstream of task X. Note that depends_on_past