code: def get_data(msa_len: int, pair_len: int) -> Tuple[List, List]:
      node = torch.randn(1, msa_len, pair_len, 256).cuda()
      node_mask = torch.randn(1, msa_len, pair_len).cuda()
      pair = torch.randn(1, pair_len, pair_len, 128).cuda()
      pair_mask = torch.randn(1, pair_len, pair_len).cuda()
  
      meta_args = [
          ("m", node),
          ("z", pair),
          ("msa_mask", node_mask),
          ("pair_mask", pair_mask),
      ]
      concrete_args = [("chunk_size", None), ("_mask_trans", True)]
      return meta_args, concrete_args
  
file_path: colossalai/tests/test_autochunk/test_autochunk_alphafold/test_autochunk_evoformer_stack.py
imports: from typing import List, Tuple
  import pytest
  import torch
  import torch.fx
  from test_autochunk_alphafold_utils import run_test
  from colossalai.autochunk.autochunk_codegen import AUTOCHUNK_AVAILABLE
  from colossalai.testing import clear_cache_before_run, parameterize, spawn
label_desc: Use image node_id 2 for calling a global function with example usage: get_data(msa_len, pair_len) and returns: meta_args,  concrete_args