code: def test_reshape_parallel(randX_20_10, backend, io_type):
      model = tf.keras.models.Sequential(
          [
              tf.keras.layers.Input(shape=(10,)),
              tf.keras.layers.Dense(10 * 3),
              tf.keras.layers.Reshape((10, 3)),
              tf.keras.layers.ReLU(),
          ]
      )
      model.compile(optimizer="adam", loss="mse")
      config = hls4ml.utils.config_from_keras_model(model)
      prj_name = f"hls4mlprj_reshape_{backend}_{io_type}"
      output_dir = str(test_root_path / prj_name)
      hls_model = hls4ml.converters.convert_from_keras_model(
          model,
          hls_config=config,
          output_dir=output_dir,
          io_type=io_type,
          backend=backend,
      )
      hls_model.compile()
  
      X = randX_20_10
      y_qkeras = model.predict(X)
      y_hls4ml = hls_model.predict(X)
  
      # check that the values are close
      np.testing.assert_allclose(
          y_qkeras.ravel(), y_hls4ml.ravel(), atol=0.02
      )
  
file_path: hls4ml/test/pytest/test_reshape.py
imports: from pathlib import Path
  import numpy
  import pytest
  import tensorflow
  import hls4ml
label_desc: Use image node_id 3 for calling a global function with example usage: test_reshape_parallel(randX_20_10, backend, io_type) without return types