**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
condition = super().execute(context)
        self.log.info("Condition result is %s", condition)
        if condition:
            self.log.info('Proceeding with downstream tasks...')
            return
        self.log.info('Skipping downstream tasks...')
        downstream_tasks = context['task'].get_flat_relatives(upstream=False)
        self.log.debug("Downstream task_ids %s", downstream_tasks)
        if downstream_tasks:
            self.skip(context['dag_run'], context['ti'].execution_date, downstream_tasks)
        self.log.info("Done.")
class PythonVirtualenvOperator(PythonOperator):
    """
    Allows one to run a function in a virtualenv that is created and destroyed
    automatically (with certain caveats).
    The function must be defined using def, and not be
    part of a class. All imports must happen inside the function
    and no variables outside of the scope may be referenced. A global scope
    variable named virtualenv_string_args will be available (populated by
    string_args). In addition, one can pass stuff through op_args and op_kwargs, and one
    can use a return value.
    Note that if your virtualenv runs in a different Python major version than Airflow,
    you cannot use return values, op_args, or op_kwargs. You can use string_args though.
    .. seealso::