**Description**

Similarly to `@dag` and `@task` decorators, we can add a decorator to easily generate `TaskGroup` instances. 

**Use case / motivation**

This would be used for reusable pieces of DAGs. An example may be to hyperparameter tune an ML pipeline. You may want to generate several parallel subpipelines with different settings/parameters to be executed in parallel. At the end pull the result and decide the best parametrization.  

```py
@task
collect_dataset(...)
  pass

@task
def train_model(...)
   pass

@task_group
def movielens_model_pipeline(learning_rate: int, feature_list: list, dataset: XComArg):  
  dataset = filter_dataset(dataset, feature_list)
  train_model(dataset, learning_rate)

@dag
def movielens_hpt(dataset_path: str, feature_list:list=['year', 'director']):
  dataset = load_dataset(dataset_path)
  for i in range(0.1, 0.9, step=0.1):
    movielens_model_pipeline(i, feature_list, dataset)
  
  decide_best_model(...)
```




**Related Issues**

https://github.com/apache/airflow/issues?q=is%3Aopen+is%3Aissue+label%3AAIP-31


tests/utils/test_task_group.py
#
#
#
import pendulum
import pytest
from airflow.models import DAG
from airflow.operators.dummy import DummyOperator
from airflow.utils.task_group import TaskGroup
from airflow.www.views import dag_edges, task_group_to_dict
EXPECTED_JSON = {
    'id': None,
    'value': {
        'label': None,
        'labelStyle': 'fill:#000;',
        'style': 'fill:CornflowerBlue',
        'rx': 5,
        'ry': 5,
        'clusterLabelPos': 'top',
    },
    'tooltip': '',
    'children': [
        {
            'id': 'group234',
            'value': {
                'label': 'group234',
                'labelStyle': 'fill:#000;',
                'style': 'fill:CornflowerBlue',
                'rx': 5,
                'ry': 5,
                'clusterLabelPos': 'top',