code: def test_repack(backend: str, strategy: str):
      inp1 = keras.Input(shape=(4,), name="inp1")
      inp2 = keras.Input(shape=(4,), name="inp2")
      r1 = keras.layers.Reshape((2, 2), name="reshape1")(inp1)
      r2 = keras.layers.Reshape((2, 2), name="reshape2")(inp2)
      out = keras.layers.Concatenate(name="concat")([r1, r2])
      model = keras.Model([inp1, inp2], out)
  
      hls_config = {
          "Model": {"Precision": "ap_ufixed<8,8>", "ReuseFactor": 1},
          "Strategy": strategy,
      }
      model_hls = convert_from_keras_model(
          model,
          io_type="io_stream",
          backend=backend,
          hls_config=hls_config,
          output_dir=str(
              test_root_path / f"hls4mlprj_repack_{backend}_{strategy}"
          ),
      )
      model_hls.compile()
      inp_data = [
          np.random.randint(0, 2**8, (100, 4)).astype(np.float32),
          np.random.randint(0, 2**8, (100, 4)).astype(np.float32),
      ]
      out_target = np.concatenate(
          [
              inp_data[0].reshape(100, 2, 2),
              inp_data[1].reshape(100, 2, 2),
          ],
          axis=-1,
      )
      out_data: np.ndarray = model_hls.predict(inp_data)  # type: ignore
      assert np.all(
          out_data.reshape(out_target.shape) == out_target
      ), "Concatenate failed: mismatching output"
  
imports: from pathlib import Path
  import numpy
  import pytest
  from tensorflow import keras
  from hls4ml.converters import convert_from_keras_model
label_desc: Use image node_id 2 for calling a global function with example usage: test_repack(backend, strategy) without return types