"""Create recovery_planner
Generated by Phi-Octopus Eden
2025-11-07 19:03:33.120293
"""

```python
from typing import List, Dict


class RecoveryPlanner:
    """
    A class for creating a limited error recovery planner.
    
    This planner will attempt to recover from errors by retrying operations up to a certain number of times,
    with exponential backoff between retries. It is useful in scenarios where transient failures are common.
    """

    def __init__(self, max_retries: int = 3, base_delay: float = 1):
        """
        Initialize the RecoveryPlanner.

        :param max_retries: Maximum number of times to retry an operation before failing.
        :param base_delay: The initial delay in seconds between retries. Default is 1 second.
        """
        self.max_retries = max_retries
        self.base_delay = base_delay

    def plan_recovery(self, func: callable) -> callable:
        """
        Decorator to wrap a function with recovery logic.

        :param func: The function to be wrapped and potentially retried.
        :return: A new function that includes error recovery.
        """

        def wrapper(*args, **kwargs):
            current_retry = 0
            while current_retry <= self.max_retries:
                try:
                    return func(*args, **kwargs)
                except Exception as e:
                    print(f"Error occurred: {e}. Retrying...")
                    if current_retry < self.max_retries:
                        delay = self.base_delay * (2 ** current_retry)
                        print(f"Retrying in {delay} seconds.")
                        import time
                        time.sleep(delay)
                    current_retry += 1
            raise Exception("Max retries reached. Giving up.")

        return wrapper

# Example usage:

@RecoveryPlanner(max_retries=3, base_delay=2).plan_recovery
def risky_function() -> str:
    """
    A risky function that may fail with a random probability.
    
    This is just an example and will not actually fail in practice.
    """
    import random
    if random.randint(0, 1) == 0:  # Simulate failure
        raise ValueError("Simulated error")
    return "Success"

if __name__ == "__main__":
    try:
        result = risky_function()
        print(f"Operation successful with result: {result}")
    except Exception as e:
        print(f"Final failure: {e}")

```

This code defines a `RecoveryPlanner` class that can be used to wrap functions and add limited error recovery. The example usage shows how to use the planner to decorate a function that may fail, with retries increasing exponentially between attempts.