code: def train(epoch):
      model.train()
  
      total_loss = 0
      for data in tqdm(train_loader):
          data = data.to(device)
          optimizer.zero_grad()
          out = model(data.x, data.pos, data.batch, data.ptr)
          loss = F.nll_loss(out, data.y)
          loss.backward()
          optimizer.step()
          total_loss += data.num_graphs * float(loss)
      return total_loss / len(train_loader.dataset)
  
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 a global function with example usage: train(epoch) and returns: unknown