class_docstr: Positional encoding of points in a neighborhood.
class_docstr_tok: ["Positional","encoding","of","points","in","a","neighborhood","."]
code: def forward(self, edge_index, x, pos):
      out = self.propagate(edge_index, x=x, pos=pos)  # N, d_out
      out = self.mlp_post_attention(out)  # N, d_out
      return out
  
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 2 for calling the LocalFeatureAggregation obj's underlying member method code with example usage: obj.forward(edge_index, x, pos) and returns: out