code: def test_keras_h5_loader(backend):
      input_shape = (10,)
      model = tf.keras.models.Sequential(
          [
              tf.keras.layers.InputLayer(input_shape=input_shape),
              tf.keras.layers.Activation(activation="relu"),
          ]
      )
  
      hls_config = hls4ml.utils.config_from_keras_model(
          model, granularity="name"
      )
  
      config = {
          "OutputDir": str(
              test_root_path
              / f"hls4mlprj_KerasH5_loader_test_{backend}"
          ),
          "ProjectName": f"KerasH5_loader_test_{backend}",
          "Backend": backend,
          "ClockPeriod": 25.0,
          "IOType": "io_parallel",
          "HLSConfig": hls_config,
          "KerasH5": str(
              test_root_path
              / f"hls4mlprj_KerasH5_loader_test_{backend}/model.h5"
          ),
      }
  
      model.save(config["KerasH5"])
      hls_model = hls4ml.converters.keras_to_hls(config)
      hls_model.compile()
      data = np.random.rand(1000, 10).astype(np.float32)
      pred = hls_model.predict(data)
      np.testing.assert_allclose(
          pred, model.predict(data), rtol=5e-3, atol=5e-3
      )
  
imports: from pathlib import Path
  import numpy
  import pytest
  import tensorflow
  import hls4ml
label_desc: Use image node_id 1 for calling a global function with example usage: test_keras_h5_loader(backend) without return types