**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


airflow/utils/task_group.py
self, task_or_task_list: Union[TaskMixin, Sequence[TaskMixin]], upstream: bool = False
    ) -> None:
        """
        Call set_upstream/set_downstream for all root/leaf tasks within this TaskGroup.
        Update upstream_group_ids/downstream_group_ids/upstream_task_ids/downstream_task_ids.
        """
        if upstream:
            for task in self.get_roots():
                task.set_upstream(task_or_task_list)
        else:
            for task in self.get_leaves():
                task.set_downstream(task_or_task_list)
        if not isinstance(task_or_task_list, Sequence):
            task_or_task_list = [task_or_task_list]
        for task_like in task_or_task_list:
            self.update_relative(task_like, upstream)
    def set_downstream(self, task_or_task_list: Union[TaskMixin, Sequence[TaskMixin]]) -> None: