text: Title: Unclear usage of the PNA model
  Body: ### 📚 Describe the documentation issue
  
  https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.models.PNA.html
  It's listed here that the **non-optional** arguments are:
  **in_channels, hidden_channels, num_layers, out_channels** 
  which is consistent with the other models (GIN-GCN) that create the layers under the hood.
  
  However, PNA requires the presence of 'aggregators', 'scaler', 'degree' making them **non-optional** arguments.
  ```
          **kwargs (optional): Additional arguments of
              :class:`torch_geometric.nn.conv.PNAConv`.
  ```
  
  
  ### Suggest a potential alternative/fix
  
  A simple fix would be:
  ```
      def init_conv(self, in_channels: int, out_channels: int,
                    **kwargs) -> MessagePassing:
          
         ## For the deg argument ##
         The init must recieve the deg argument
  
         ## Solution-1 for the aggregators/scalers ##
          Defined here:
           aggregators = ['mean', 'min', 'max', 'std']
           scalers = ['identity', 'amplification', 'attenuation']
           return PNAConv(in_channels, out_channels, aggregators = aggregators , scalers  = scalers )
  ```
  Alternatively, default the constructor of PNAConv to use these lists if nothing is defined.
  
  
  
  