code: def test_get_landmark_llama():
      import torch
      from transformers import AutoTokenizer, AutoModelForCausalLM
      from transformers import LlamaForCausalLM, LlamaTokenizer
  
      m = LlamaForCausalLM.from_pretrained(
          "epfml/landmark-attention-llama7b-wdiff"
      )
      t = LlamaTokenizer.from_pretrained(
          "epfml/landmark-attention-llama7b-wdiff"
      )
      assert m is not None and t is not None
  
      os.system(
          """
  #
  # step 1, convert llama to HF format
  pip install protobuf==3.19.0
  source ~/.bashrc.mamba
  mamba create -n trans
  conda activate trans
  conda install python=3.10 -y
  
  git clone https://github.com/epfml/landmark-attention.git
  pip install fire datasets
  git clone https://github.com/huggingface/transformers.git
  cd transformers
  pip install .
  pip install torch accelerate sentencepiece protobuf==3.19.0
  # below requires LLaMa weights
  python src/transformers/models/llama/convert_llama_weights_to_hf.py     --input_dir /data/jon/LLaMA --model_size 7B --output_dir llama_7B
  #
  # step 2, make landmark model (change hash if updated)
  mkdir -p epfml/landmark-attention-llama7b-wdiff
  cd epfml/landmark-attention-llama7b-wdiff
  ln -s ~/.cache/huggingface/hub/models--epfml--landmark-attention-llama7b-wdiff/snapshots/050562871ac72723b4ab674f0392b02cd9609842/* .
  cd ../../
  python ../landmark-attention/llama/weight_diff.py recover --path_raw llama_7B --path_diff epfml/landmark-attention-llama7b-wdiff --path_tuned landmark_llama_7b
  """
      )
  
imports: import os
  import pytest
  from tests.utils import wrap_test_forked
label_desc: Use image node_id 3 for calling a global function with example usage: test_get_landmark_llama() without return types