**Apache Airflow version**:
2.0 / master

**Environment**:

breeze

**What happened**:

Using this DAG:

```py
from airflow.operators.bash import BashOperator
from airflow.operators.python import task
from airflow.models import DAG
from airflow.utils.task_group import TaskGroup

@task
def show():
    print("Cats are awesome!")

with DAG(
    "using_task_group",
    default_args={'owner': 'airflow'},
    start_date=days_ago(2),
    schedule_interval=None,
) as dag3:
    start_task = BashOperator(
        task_id="start_task",
        bash_command="echo start",
    )

    end_task = BashOperator(
        task_id="end_task",
        bash_command="echo end",
    )

    with TaskGroup(group_id="show_tasks") as tg1:
        previous_show = show()
        for _ in range(100):
            next_show = show()
            previous_show >> next_show
            previous_show = next_show
```
I get:
```
Broken DAG: [/files/dags/test.py] Traceback (most recent call last):
  File "/opt/airflow/airflow/models/baseoperator.py", line 410, in __init__
    task_group.add(self)
  File "/opt/airflow/airflow/utils/task_group.py", line 140, in add
    raise DuplicateTaskIdFound(f"Task id '{key}' has already been added to the DAG")
airflow.exceptions.DuplicateTaskIdFound: Task id 'show_tasks.show' has already been added to the DAG
```

If I remove the task group the task are generated as expected.

**What you expected to happen**:

I expect to be able to generate tasks dynamically using TaskGroup and task decoratos.

**How to reproduce it**:

Use the DAG from above.

**Anything else we need to know**:

N/A

airflow/operators/python.py
"""
    Allows a workflow to "branch" or follow a path following the execution
    of this task.
    It derives the PythonOperator and expects a Python function that returns
    a single task_id or list of task_ids to follow. The task_id(s) returned
    should point to a task directly downstream from {self}. All other "branches"
    or directly downstream tasks are marked with a state of ``skipped`` so that
    these paths can't move forward. The ``skipped`` states are propagated
    downstream to allow for the DAG state to fill up and the DAG run's state
    to be inferred.
    """
    def execute(self, context: Dict):
        branch = super().execute(context)
        self.skip_all_except(context['ti'], branch)
        return branch
class ShortCircuitOperator(PythonOperator, SkipMixin):
    """
    Allows a workflow to continue only if a condition is met. Otherwise, the
    workflow "short-circuits" and downstream tasks are skipped.
    The ShortCircuitOperator is derived from the PythonOperator. It evaluates a
    condition and short-circuits the workflow if the condition is False. Any
    downstream tasks are marked with a state of "skipped". If the condition is
    True, downstream tasks proceed as normal.
    The condition is determined by the result of `python_callable`.
    """
    def execute(self, context: Dict):