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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**:

<!-- (please include exact error messages if you can) -->

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

**What you expected to happen**:

<!-- What do you think went wrong? -->

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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As minimally and precisely as possible. Keep in mind we do not have access to your cluster or dags.

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

## Install minikube/kind

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

If this is a UI bug, please provide a screenshot of the bug or a link to a youtube video of the bug in action

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airflow/models/baseoperator.py
significantly speeding up the task creation process as for very large
        DAGS. Options can be set as string or using the constants defined in
        the static class ``airflow.utils.WeightRule``
    :type weight_rule: str
    :param queue: which queue to target when running this job. Not
        all executors implement queue management, the CeleryExecutor
        does support targeting specific queues.
    :type queue: str
    :param pool: the slot pool this task should run in, slot pools are a
        way to limit concurrency for certain tasks
    :type pool: str
    :param pool_slots: the number of pool slots this task should use (>= 1)
        Values less than 1 are not allowed.
    :type pool_slots: int
    :param sla: time by which the job is expected to succeed. Note that
        this represents the ``timedelta`` after the period is closed. For
        example if you set an SLA of 1 hour, the scheduler would send an email
        soon after 1:00AM on the ``2016-01-02`` if the ``2016-01-01`` instance
        has not succeeded yet.
        The scheduler pays special attention for jobs with an SLA and
        sends alert
        emails for sla misses. SLA misses are also recorded in the database
        for future reference. All tasks that share the same SLA time
        get bundled in a single email, sent soon after that time. SLA
        notification are sent once and only once for each task instance.
    :type sla: datetime.timedelta
    :param execution_timeout: max time allowed for the execution of
        this task instance, if it goes beyond it will raise and fail.
    :type execution_timeout: datetime.timedelta
    :param on_failure_callback: a function to be called when a task instance