code: def test_ghostbatchnormalization(
      mode: bool, virtual_batch_size: Optional[int]
  ) -> None:
      # setup up GhostBatchNormalization layer
      ghost_batch_norm = GhostBatchNormalization(
          OUTPUT_SIZE, virtual_batch_size=virtual_batch_size
      )
  
      # set training or eval mode
      ghost_batch_norm.train(mode=mode)
  
      # setup inputs to test
      inputs = torch.randn(
          [BATCH_SIZE, OUTPUT_SIZE], dtype=torch.float32
      )
  
      # run tensor through
      norm_tensor = ghost_batch_norm(inputs)
  
      # check for correctness of output
      assert isinstance(norm_tensor, torch.Tensor)
      assert norm_tensor.shape == (BATCH_SIZE, OUTPUT_SIZE)
  
      # check for required properties
      assert ghost_batch_norm.input_shape == inputs.shape[1:]
      assert ghost_batch_norm.output_shape == inputs.shape[1:]
      assert ghost_batch_norm.input_dtype == torch.float32
  
      assert isinstance(ghost_batch_norm.moving_mean, torch.Tensor)
      assert ghost_batch_norm.moving_mean.shape == (OUTPUT_SIZE,)
  
      assert isinstance(ghost_batch_norm.moving_variance, torch.Tensor)
      assert ghost_batch_norm.moving_variance.shape == (OUTPUT_SIZE,)
  
imports: from typing import Optional
  import pytest
  import torch
  from ludwig.modules.normalization_modules import GhostBatchNormalization
label_desc: Use image node_id 1 for calling a global function with example usage: test_ghostbatchnormalization(mode, virtual_batch_size) without return types