class_docstr: Implements both the Local Spatial Encoding and Attentive Pooling blocks from
  RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
  https://arxiv.org/pdf/1911.11236
code: def update(self, aggr_out):
      return self.global_nn(aggr_out)
  
imports: import torch
  import torch.nn.functional
  from torch_geometric.nn import MessagePassing
  from torch_points3d.core.spatial_ops import *
  from torch_points3d.core.base_conv.message_passing import *
label_desc: Use image node_id 4 for calling the RandlaKernel obj's underlying member method code with example usage: obj.update(aggr_out) and returns: self