code: def test_repack_precision(backend: str):
      inp = keras.Input(shape=(3, 3), name="inp")
      out = keras.layers.Reshape((3, 3), name="reshape")(inp)
      out = keras.layers.Conv1D(2, 2, name="conv")(out)
      model = keras.Model(inp, out)
  
      layer_conf = {
          "inp": {"Precision": "fixed<20,10>"},
          "reshape": {"Precision": "fixed<20,10>"},
          "conv": {"Precision": "fixed<20,10>"},
      }
  
      hls_config = {
          "Model": {"Precision": "fixed<2,1>", "ReuseFactor": 1},
          "LayerName": layer_conf,
      }
  
      # Repack only happens in io_stream
      model_hls = convert_from_keras_model(
          model,
          backend=backend,
          output_dir=str(
              test_root_path / f"hls4mlprj_repack_precision_{backend}"
          ),
          hls_config=hls_config,
          io_type="io_stream",
      )
      model_hls.write()  # Not needed for this test, but useful for debugging
      assert (
          "repack_reshape" in model_hls.graph
      ), "repack_reshape not found in graph"
      repack_precision = (
          model_hls.graph["repack_reshape"]
          .attributes["result_t"]
          .precision
      )
      assert repack_precision.integer == 10, "Precision mismatch"
      assert repack_precision.fractional == 10, "Precision mismatch"
      assert repack_precision.width == 20, "Precision mismatch"
      assert repack_precision.signed is True, "Precision mismatch"
  
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 1 for calling a global function with example usage: test_repack_precision(backend) without return types