code: def forward(self, x, pos, batch, ptr):
      x = x if x is not None else pos
      b1 = self.block1(self.fc0(x), pos, batch)
      b1_decimated, ptr1 = decimate(b1, ptr, self.decimation)
  
      b2 = self.block2(*b1_decimated)
      b2_decimated, _ = decimate(b2, ptr1, self.decimation)
  
      x = self.mlp1(b2_decimated[0])
      x = self.max_agg(x, b2_decimated[2])
  
      x = self.mlp_classif(x)
      logits = self.fc_classif(x)
  
      return (
          logits if self.return_logits else logits.log_softmax(dim=-1)
      )
  
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 Net obj's underlying member method code with example usage: obj.forward(x, pos, batch, ptr) and returns: unknown