code: def test_model_forward_pass() -> None:
      opts = get_opts()
      model = byteformer.ByteFormer(opts)
  
      B, N = 1, 256
      x_values = torch.empty([B, N], dtype=torch.int)
      x_values[:, :129] = torch.randint(0, 256, size=(1, 129))
      x_values[:, 129:] = -1
  
      y = model(x_values)
      assert y.shape == (1, 1000)
  
imports: import argparse
  import torch
  from cvnets.models.classification import byteformer
label_desc: Use image node_id 3 for calling a global function with example usage: test_model_forward_pass() without return types