code: def __init__(
      self, local_nn, global_nn, aggr="max", return_local_out=False
  ):
      super().__init__()
  
      self._local_nn = MLP(local_nn)
      self._global_nn = MLP(global_nn) if global_nn else None
      self._aggr = aggr
      self.g_pool = (
          global_max_pool if aggr == "max" else global_mean_pool
      )
      self.return_local_out = return_local_out
  
imports: import torch
  from torch_geometric.nn import global_max_pool, global_mean_pool
  from torch_points3d.core.common_modules.base_modules import *
  from torch_points3d.core.common_modules.spatial_transform import BaseLinearTransformSTNkD
  from torch_points3d.models.base_model import BaseInternalLossModule
label_desc: Use image node_id 1 to create a new MiniPointNet object from inherited base classes: torch.nn with example: obj = MiniPointNet(local_nn, global_nn, aggr, return_local_out)