code: def training_step(self, data: Data, batch_idx: int):
      y_hat = self(data.x)[data.train_mask]
      y = data.y[data.train_mask]
      loss = F.cross_entropy(y_hat, y)
      self.train_acc(y_hat.softmax(dim=-1), y)
      self.log("loss", loss, batch_size=y.size(0))
      self.log("train_acc", self.train_acc, batch_size=y.size(0))
      return loss
  
imports: import math
  import pytest
  import torch
  import torch.nn.functional
  from torch import Tensor
  from torch_geometric.data import Data, HeteroData
  from torch_geometric.data.lightning import LightningDataset, LightningLinkData, LightningNodeData
  from torch_geometric.nn import global_mean_pool
  from torch_geometric.sampler import BaseSampler, NeighborSampler
  from torch_geometric.testing import MyFeatureStore, MyGraphStore, get_random_edge_index, onlyCUDA, onlyFullTest, onlyNeighborSampler, onlyOnline, withPackage
label_desc: Use image node_id 3 for calling the LinearNodeModule obj's underlying member method code with example usage: obj.training_step(data, batch_idx) and returns: loss