**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,
        group_id: Optional[str],
        prefix_group_id: bool = True,
        parent_group: Optional["TaskGroup"] = None,
        dag: Optional["DAG"] = None,
        tooltip: str = "",
        ui_color: str = "CornflowerBlue",
        ui_fgcolor: str = "#000",
    ):
        from airflow.models.dag import DagContext
        self.prefix_group_id = prefix_group_id
        if group_id is None:
            
            if parent_group:
                raise AirflowException("Root TaskGroup cannot have parent_group")
            
            
            self.used_group_ids: Set[Optional[str]] = set()
            self._parent_group = None
        else:
            if not isinstance(group_id, str):