code: def __init__(self, in_channels: int, out_channels: int):
      super().__init__()
      from torchmetrics import Accuracy
  
      self.lin = torch.nn.Linear(in_channels, out_channels)
  
      self.train_acc = Accuracy(
          task="multiclass", num_classes=out_channels
      )
      self.val_acc = Accuracy(
          task="multiclass", num_classes=out_channels
      )
      self.test_acc = Accuracy(
          task="multiclass", num_classes=out_channels
      )
  
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 1 to create a new LinearNodeModule object from inherited base classes: LightningModule with example: obj = LinearNodeModule(in_channels, out_channels)