text: Title: [ENH] Integrate pypots for Partially Observed Time Series (POTS) as Imputers in sktime
  Body: **Is your feature request related to a problem? Please describe.**
  Handling Partially Observed Time Series (POTS) is a common challenge in real-world scenarious.
  Currently **Sktime** lacks the native support for models specifically designed to handle POTS
  The **pypots** library offers specialized models for imputation and forecasting on partially observed time series.
  Integrating **pypots** into sktime would extend its capabilities, for users dealing with incomplete datasets, and provide more robust solutions for missing data..
  
  **Describe the solution you'd like**
  Integrate **pyplots** into sktime as part of **series-to-series** transformers network, specifically under **imputers**. Since many pypost models can also be used for forecasting, the integration should consider the following:
  - Designing the interface so that pypots models can act as imputers while enabling potential forecasting use cases.
  - Investigating if common neural network components, in pypots can be factored out to create multi-purpose models (usable for both imputation and forecasting)
  - Evaluating whether pypots is compatible with imputation-by-forecasting strategies, and if so, aligning the integration with sktime's forecasting API, which will serve the users with dual features..
  
  For context, you can checkout this official documentation of `PyPots` : https://pypots.com/
  Also you can checkout their CodeBase: https://github.com/WenjieDu/PyPOTS
  Additionally, you can checkout the BaseImputer class (which needs to interfaced) : https://github.com/WenjieDu/PyPOTS/blob/8c89c1a19ef0d7d7e8bcbbba594c0956fa2ea81e/pypots/imputation/base.py#L4
  