  ```
import qlib

class OnlineManager:
    def __init__(self, provider_uri, region, experiment_name, task_url, task_database_name, task_pool_name, rolling_step, start_time, end_time, tasks, trainer):
        self.provider_uri = provider_uri
        self.region = region
        self.experiment_name = experiment_name
        self.task_url = task_url
        self.task_database_name = task_database_name
        self.task_pool_name = task_pool_name
        self.rolling_step = rolling_step
        self.start_time = start_time
        self.end_time = end_time
        self.tasks = tasks
        self.trainer = trainer

    def reset_experiment(self):
        # Reset the experiment by deleting the task database and task pool
        qlib.delete_database(self.task_database_name)
        qlib.delete_pool(self.task_pool_name)

    def run_workflow(self):
        # Run the entire workflow automatically
        # 1. Create the task database and task pool
        qlib.create_database(self.task_database_name)
        qlib.create_pool(self.task_pool_name)
        # 2. Create the tasks
        for task in self.tasks:
            qlib.create_task(task, self.task_database_name, self.task_pool_name)
        # 3. Start the tasks
        qlib.start_tasks(self.task_pool_name)
        # 4. Wait for the tasks to complete
        qlib.wait_tasks(self.task_pool_name)
        # 5. Collect the results
        results = qlib.collect_results(self.task_database_name)
        # 6. Get the signals
        signals = qlib.get_signals(results)
        # 7. Perform backtesting
        backtest_results = qlib.backtest(signals, self.start_time, self.end_time)
        # 8. Perform risk analysis
        risk_analysis_results = qlib.risk_analysis(backtest_results)
        return risk_analysis_results

    def train_tasks(self, processes):
        # Train tasks by other processes or machines for multiprocessing
        # 1. Create the task database and task pool
        qlib.create_database(self.task_database_name)
        qlib.create_pool(self.task_pool_name)
        # 2. Create the tasks
        for task in self.tasks:
            qlib.create_task(task, self.task_database_name, self.task_pool_name)
        # 3. Start the tasks
        qlib.start_tasks(self.task_pool_name)
        # 4. Wait for the tasks to complete
        qlib.wait_tasks(self.task_pool_name)
        # 5. Collect the results
        results = qlib.collect_results(self.task_database_name)
        # 6. Get the signals
        signals = qlib.get_signals(results)
        # 7. Perform backtesting
        backtest_results = qlib.backtest(signals, self.start_time, self.end_time)
        # 8. Perform risk analysis
        risk_analysis_results = qlib.risk_analysis(backtest_results)
        return risk_analysis_results

if __name__ == "__main__":
    # Initialize the OnlineManager with user-defined parameters
    provider_uri = "https://qlib.ai"
    region = "us-east-1"
    experiment_name = "my_experiment"
    task_url = "https://my-task-url.com"
    task_database_name = "my_task_database"
    task_pool_name = "my_task_pool"
    rolling_step = 10
    start_time = "2022-01-01"
    end_time = "2022-01-31"
    tasks = ["task1", "task2", "task3"]
    trainer = "my_trainer"
    online_manager = OnlineManager(provider_uri, region, experiment_name, task_url, task_database_name, task_pool_name, rolling_step, start_time, end_time, tasks, trainer)

    # Reset the experiment
    online_manager.reset_experiment()

    # Run the entire workflow automatically
    results = online_manager.run_workflow()

    # Train tasks by other processes or machines for multiprocessing
    results = online_manager.train_tasks(4)

    # Print the results
    print(results)
```