**Apache Airflow version**: 1.10.14

**Environment**:

- **Cloud provider or hardware configuration**: docker
- **OS** (e.g. from /etc/os-release): apache/airflow:1.10.14-python3.8
- **Kernel** (e.g. `uname -a`): Linux host 5.4.0-62-generic #70-Ubuntu SMP Tue Jan 12 12:45:47 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux
- **Install tools**: Docker version 19.03.8, build afacb8b7f0
- **Others**:

**What happened**:
I created a DAG and set the doc_md property on the object but it isn't being rendered in the UI.

**What you expected to happen**:
I expected the markdown to be rendered in the UI

**How to reproduce it**:
Created a new container using the `airflow:1.10.14`, I have tried the following images with the same results.

-  airflow:1.10.14:image-python3.8
-  airflow:1.10.14:image-python3.7
-  airflow:1.10.12:image-python3.7
-  airflow:1.10.12:image-python3.7

```
dag_docs = """
## Pipeline

#### Purpose
This is a pipeline
"""

dag = DAG(
    'etl-get_from_api',
    default_args=default_args,
    description='A simple dag',
    schedule_interval=timedelta(days=1),
)
dag.doc_md = dag_docs

```

![image](https://user-images.githubusercontent.com/29732449/105004686-6b77d680-5a88-11eb-9e34-c8dd38b3fd10.png)

![image](https://user-images.githubusercontent.com/29732449/105004748-7af71f80-5a88-11eb-811c-11bc6a351c71.png)

I have also tried with using a doc-string to populate the doc_md as well as adding some text within the constructor.

```
dag = DAG(
    'etl-get_from_api',
    default_args=default_args,
    description='A simple dag',
    schedule_interval=timedelta(days=1),
    doc_md = "some text"
)
```
All of the different permutations I've tried seem to have the same result. The only thing I can change is the description, that appears to show up correctly.

**Anything else we need to know**:
I have tried multiple browsers (Firefox and Chrome) and I have also done an inspect on from both the graph view and the tree view from within the dag but I can't find any of the text within the page at all.

airflow/models/baseoperator.py
is forced to True wherever wait_for_downstream is used. Also note that
        only tasks *immediately* downstream of the previous task instance are waited
        for; the statuses of any tasks further downstream are ignored.
    :type wait_for_downstream: bool
    :param dag: a reference to the dag the task is attached to (if any)
    :type dag: airflow.models.DAG
    :param priority_weight: priority weight of this task against other task.
        This allows the executor to trigger higher priority tasks before
        others when things get backed up. Set priority_weight as a higher
        number for more important tasks.
    :type priority_weight: int
    :param weight_rule: weighting method used for the effective total
        priority weight of the task. Options are:
        ``{ downstream | upstream | absolute }`` default is ``downstream``
        When set to ``downstream`` the effective weight of the task is the
        aggregate sum of all downstream descendants. As a result, upstream
        tasks will have higher weight and will be scheduled more aggressively
        when using positive weight values. This is useful when you have
        multiple dag run instances and desire to have all upstream tasks to
        complete for all runs before each dag can continue processing
        downstream tasks. When set to ``upstream`` the effective weight is the
        aggregate sum of all upstream ancestors. This is the opposite where
        downstream tasks have higher weight and will be scheduled more
        aggressively when using positive weight values. This is useful when you
        have multiple dag run instances and prefer to have each dag complete
        before starting upstream tasks of other dags.  When set to
        ``absolute``, the effective weight is the exact ``priority_weight``
        specified without additional weighting. You may want to do this when
        you know exactly what priority weight each task should have.
        Additionally, when set to ``absolute``, there is bonus effect of