**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
"""
    Test that if a task doesn't have a DAG when it's being set as the relative of another task which
    has a DAG, the task should be added to the root TaskGroup of the other task's DAG.
    """
    dag = DAG(dag_id='test_task_without_dag', start_date=pendulum.parse("20200101"))
    op1 = DummyOperator(task_id='op1', dag=dag)
    op2 = DummyOperator(task_id='op2')
    op3 = DummyOperator(task_id="op3")
    op1 >> op2
    op3 >> op2
    assert op1.dag == op2.dag == op3.dag
    assert dag.task_group.children.keys() == {"op1", "op2", "op3"}
    assert dag.task_group.children.keys() == dag.task_dict.keys()