code: def test_linear_classifier_parameter_initialization():
      in_dim = 10
      out_dim = 5
      set_seed(2021)
      model = LinearClassifier(in_dim, out_dim)
      for name, param in model.named_parameters():
          if "bias" in name:
              assert torch.allclose(
                  param.data, torch.zeros_like(param.data)
              )
          else:
              assert param.std().detach().numpy() == pytest.approx(
                  1 / in_dim**0.5, rel=1e-1
              )
  
file_path: pykale/tests/predict/test_decode.py
imports: import pytest
  import torch
  from kale.predict.decode import LinearClassifier, MLPDecoder, VCDN
  from kale.utils.seed import set_seed
label_desc: Use image node_id 4 for calling a global function with example usage: test_linear_classifier_parameter_initialization() without return types