code: def test_BatchNorm1d(device):
      from speechbrain.nnet.normalization import BatchNorm1d
  
      input = torch.randn(100, 10, device=device) + 2.0
      norm = BatchNorm1d(input_shape=input.shape).to(device)
      output = norm(input)
      assert input.shape == output.shape
  
      current_mean = output.mean(dim=0).mean()
      assert torch.abs(current_mean) < 1e-06
  
      current_std = output.std(dim=0).mean()
      assert torch.abs(1.0 - current_std) < 0.01
  
      input = torch.randn(100, 20, 10, device=device) + 2.0
      output = norm(input)
      assert input.shape == output.shape
  
      current_mean = output.mean(dim=0).mean()
      assert torch.abs(current_mean) < 1e-06
  
      current_std = output.std(dim=0).mean()
      assert torch.abs(1.0 - current_std) < 0.01
  
      # Test with combined dimensions
      input = torch.randn(100, 10, 20, device=device) + 2.0
      norm = BatchNorm1d(
          input_shape=input.shape, combine_batch_time=True
      ).to(device)
      output = norm(input)
      assert input.shape == output.shape
  
      current_mean = output.mean(dim=0).mean()
      assert torch.abs(current_mean) < 1e-06
  
      current_std = output.std(dim=0).mean()
      assert torch.abs(1.0 - current_std) < 0.01
  
      input = torch.randn(100, 40, 20, 30, device=device) + 2.0
      norm = BatchNorm1d(
          input_shape=input.shape, combine_batch_time=True
      ).to(device)
      output = norm(input)
      assert input.shape == output.shape
  
      current_mean = output.mean(dim=0).mean()
      assert torch.abs(current_mean) < 1e-06
  
      current_std = output.std(dim=0).mean()
      assert torch.abs(1.0 - current_std) < 0.01
  
      assert torch.jit.trace(norm, input)
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 for calling a global function with example usage: test_BatchNorm1d(device) without return types