class_docstr: Positional encoding of points in a neighborhood.
class_docstr_tok: ["Positional","encoding","of","points","in","a","neighborhood","."]
code: def __init__(self, channels):
      super().__init__(aggr="add")
      self.mlp_encoder = SharedMLP([10, channels // 2])
      self.mlp_attention = SharedMLP(
          [channels, channels], bias=False, act=None, norm=None
      )
      self.mlp_post_attention = SharedMLP([channels, channels])
  
imports: import os.path
  import torch
  import torch.nn.functional
  from torch import Tensor
  from torch.nn import Linear
  from tqdm import tqdm
  import torch_geometric.transforms
  from torch_geometric.datasets import ModelNet
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import MLP
  from torch_geometric.nn.aggr import MaxAggregation
  from torch_geometric.nn.conv import MessagePassing
  from torch_geometric.nn.pool import knn_graph
  from torch_geometric.nn.pool.decimation import decimation_indices
  from torch_geometric.typing import WITH_TORCH_CLUSTER
  from torch_geometric.utils import softmax
label_desc: Use image node_id 1 to create a new LocalFeatureAggregation object from inherited base classes: MessagePassing with example: obj = LocalFeatureAggregation(channels)