def METHOD_NAME(src_model, src_dir, tgt_model, tgt_dir):
    state = torch.load(src_dir)
    src_model.load_state_dict(state)
    src_state = src_model.state_dict()
    # only need features
    keys1 = src_state.keys()
    keys1 = [k for k in src_state.keys() if k.startswith("features")]
    keys2 = tgt_model.state_dict().keys()
    assert len(keys1) == len(keys2)
    state = OrderedDict()
    for k1, k2 in zip(keys1, keys2):
        # print(k1, k2)
        state[k2] = src_state[k1]
    #diff_keys = state.keys() - target_model.state_dict().keys()
    #print("Different Keys:", diff_keys)
    # Remove unecessary keys
    #for k in diff_keys:
    #    state.pop(k)
    tgt_model.load_state_dict(state)
    torch.save(tgt_model.state_dict(), tgt_dir)