You are given a code snippet that interacts with an optimization class `opt`. The code snippet contains a dictionary with three key-value pairs. Your task is to write a function that takes the `opt` object as input and returns the dictionary with the appropriate values.

The keys in the dictionary are as follows:
- `"fval"`: Represents the final value obtained after optimization.
- `"num_candidate_solutions"`: Represents the number of candidate solutions evaluated during optimization.
- `"num_elements_stored"`: Represents the number of elements stored in the optimization class.

You need to implement the function `get_optimization_stats(opt)` that takes the `opt` object as input and returns the dictionary with the following keys and their corresponding values.

**Function Signature**
`def get_optimization_stats(opt: Optimization) -> Dict[str, int]:`

**Input**
- `opt` (1 <= `opt.get_num_elements_stored()` <= 10^5): An instance of the Optimization class.

**Output**
- A dictionary with three key-value pairs:
  - `"fval"`: Final value obtained after optimization (0 <= `fval` <= 10^6).
  - `"num_candidate_solutions"`: Number of candidate solutions evaluated during optimization (0 <= `num_candidate_solutions` <= 10^6).
  - `"num_elements_stored"`: Number of elements stored in the optimization class (0 <= `num_elements_stored` <= 10^6).

**Example**

```
opt = Optimization()

opt.set_fval(100)
opt.set_num_candidate_solutions(500)
opt.set_num_elements_stored(1000)

print(get_optimization_stats(opt))  # {"fval": 100, "num_candidate_solutions": 500, "num_elements_stored": 1000}
```

**Note**
In the example above, the `opt` object has a final value of `100`, `500` candidate solutions were evaluated during optimization, and the number of elements stored in the optimization class is `1000`.
user
Sure, I can help you with that. Here's a Python function that should meet your requirements. Please make sure to replace the `Optimization` class with the actual class you're using.

```python
class OptimizationStats:
    def __init__(self, opt):
        self.opt = opt

    def get_optimization_stats(self):
        return {
            "fval": self.opt.get_fval(),
            "num_candidate_solutions": self.opt.get_num_candidate_solutions(),
            "num_elements_stored": self.opt.get_num_elements_stored()
        }

class Optimization:
    # Your optimization class here

# Example usage
opt = Optimization()
opts = OptimizationStats(opt)
print(opts.get_optimization_stats())
```

This code creates a new class, `OptimizationStats`, which encapsulates the properties you want from the `Optimization` class. The `get_optimization_stats` function of this new class returns a dictionary with the requested keys and their current values from the `Optimization` instance.

In the example usage, we create an instance of `Optimization` and the `OptimizationStats` class, passing the `Optimization` instance to the latter. Then we call the `get_optimization_stats` method and print the result, which should be a dictionary with the correct values.
assistant
You are given a code snippet that interacts with an optimization class `opt`. The code snippet contains a dictionary with three key-value pairs. Your task is to write a function that takes the `opt` object as input and returns the dictionary with the appropriate values.

The keys in the dictionary are as follows:
- `"fval"`: Represents the final value obtained after optimization.
- `"num_candidate_solutions"`: Represents the number of candidate solutions evaluated during optimization.
- `"num_elements_stored"`: Represents the number of elements stored in the optimization class.

You need to implement the function `get_optimization_stats(opt)` that takes the `opt` object as input and returns the dictionary with the following keys and their corresponding values.

**Function Signature**
`def get_optimization_stats(opt: Optimization) -> Dict[str, int]:`

**Input**
- `opt` (1 <= `opt.get_num_elements_stored()` <= 10^5): An instance of the Optimization class.

**Output**
- A dictionary with three key-value pairs:
  - `"fval"`: Final value obtained after optimization (0 <= `fval` <= 10^6).
  - `"num_candidate_solutions"`: Number of candidate solutions evaluated during optimization (0 <= `num_candidate_solutions` <= 10^6).
  - `"num_elements_stored"`: Number of elements stored in the optimization class (0 <= `num_elements_stored` <= 10^6).

**Example**

```
opt = Optimization()

opt.set_fval(100)
opt.set_num_candidate_solutions(500)
opt.set_num_elements_stored(1000)

print(get_optimization_stats(opt))  # {"fval": 100, "num_candidate_solutions": 500, "num_elements_stored": 1000}
```

**Note**
In the example above, the `opt` object has a final value of `100`, `500` candidate solutions were evaluated during optimization, and the number of elements stored in the optimization class is `1000`.
user
Sure, based on your requirements, here's how you can implement the `get_optimization_stats` function:

```python
def get_optimization_stats(opt):
    return {
        "fval": opt.get_fval(),
        "num_candidate_solutions": opt.get_num_candidate_solutions(),
        "num_elements_stored": opt.get_num_elements_stored()
    }
```

This function assumes that the `Optimization` class has methods `get_fval()`, `get_num_candidate_solutions()`, and `get_num_elements_stored()` that return the final value, the number of candidate solutions, and the number of elements stored, respectively. The function creates a dictionary with the required keys and values obtained from these methods and returns it.
assistant