code: def test_pack_squeeze():
      offset_bit = 32
      precision = 16
      pack_num = 2
      pack_packed_num = 2
      vec1 = torch.tensor([0.1, 0.2, 0.3, 0.4, 0.5])
      vec2 = torch.tensor([0.6, 0.7, 0.8, 0.9, 1.0])
      sk, pk, coder = keygen(1024)
      a = coder.pack_floats(vec1, offset_bit, pack_num, precision)
      ea = pk.encrypt_encoded(a, obfuscate=False)
      b = coder.pack_floats(vec2, offset_bit, pack_num, precision)
      eb = pk.encrypt_encoded(b, obfuscate=False)
      ec = evaluator.add(ea, eb, pk)
  
      # pack packed encrypted
      ec_pack = evaluator.pack_squeeze(
          ec, pack_packed_num, offset_bit * 2, pk
      )
      c_pack = sk.decrypt_to_encoded(ec_pack)
      c = coder.unpack_floats(
          c_pack, offset_bit, pack_num * pack_packed_num, precision, 5
      )
      assert torch.allclose(vec1 + vec2, c, rtol=1e-3, atol=1e-3)
  
label_desc: Use image node_id 2 for calling a global function with example usage: test_pack_squeeze() without return types