text: Title: [ENH] Add PyODAdapter-implementation for IsolationForest
  Body: ### Describe the feature or idea you want to propose
  
  The [`PyODAdapter`](https://github.com/aeon-toolkit/aeon/blob/main/aeon/anomaly_detection/_pyodadapter.py) in aeon allows us to use any outlier detector from [PyOD](https://github.com/yzhao062/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 [IsolationForest method](https://github.com/yzhao062/pyod/blob/master/pyod/models/iforest.py#L49) 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:
  
  ```python
  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_