[ENH] Add PyODAdapter-implementation for COPOD

[SebastianSchmidl]

Describe the feature or idea you want to propose

The PyODAdapter in aeon allows us to use any outlier detector from PyOD, which were originally proposed for relational data, also for time series anomaly detection (TSAD). Not all detectors are equally well suited for TSAD, however. We want to represent the frequently used and competitive outlier detection techniques within the anomaly_detection module of aeon directly.

Implement the COPOD method using the PyODAdapter.

Describe your proposed solution

• Create a new file in aeon.anomaly_detection for the method
• Create a new estimator class with PyODAdapter as the parent
• Expose the algorithm's hyperparameters as constructor arguments, create the PyOD model and pass it to the super-constructor
• Document your class
• Add tests for certain edge cases if necessary

---

Example for IsolationForest:

class IsolationForest(PyODAdapter):
    """documentation ..."""
    def __init__(n_estimators: int = 100, max_samples: int | str = "auto", ..., window_size: int, stride: int):
        model = IForest(n_estimators, max_samples, ...
        super().__init__(model, window_size, stride)

    @classmethod
    def get_test_params(cls, parameter_set="default"):
        """..."""
        return {"n_estimators": 10, ...}

Describe alternatives you've considered, if relevant

No response

Additional context

No response

[notaryanramani]

Can you assign this to me?