code: def test_ghostbatchnormalization_chunk_size_2() -> None:
      """Test GhostBatchNormalization with virtual_batch_size=2 and batch_size=7 This creates chunks of size 2, 2, 2,
      1 which should be handled correctly since we should skip applying batch norm to the last chunk since it is size
      1."""
      # setup up GhostBatchNormalization layer
      ghost_batch_norm = GhostBatchNormalization(
          6, virtual_batch_size=2
      )
  
      # setup inputs to test
      inputs = torch.randn([7, 6], dtype=torch.float32)
  
      # Set to training mode
      ghost_batch_norm.train(mode=True)
  
      # run tensor through
      ghost_batch_norm(inputs)
  
imports: from typing import Optional
  import pytest
  import torch
  from ludwig.modules.normalization_modules import GhostBatchNormalization
label_desc: Use image node_id 2 for calling a global function with example usage: test_ghostbatchnormalization_chunk_size_2() without return types