code: def test_get_initializer():
      """Currently only checks for when the parameters are default case."""
      tensor_size = (2, 3)
  
      # Test for when the parameters are default
      torch.random.manual_seed(0)
      initialized_tensor = get_initializer("xavier_uniform")(
          *tensor_size, device=DEVICE
      )
  
      # Check that the tensor using the expected initialization and the same seed is identical
      default_initializer = nn.init.xavier_uniform_
      torch.random.manual_seed(0)
      default_tensor = default_initializer(
          torch.empty(*tensor_size, device=DEVICE)
      )
      assert torch.equal(initialized_tensor, default_tensor)
  
imports: import torch
  import torch.nn
  from ludwig.modules.initializer_modules import get_initializer
  from ludwig.utils.torch_utils import get_torch_device
label_desc: Use image node_id 1 for calling a global function with example usage: test_get_initializer() without return types