code: def test_onnx_predict(tmp_path):
      model = tf.keras.Sequential()
      model.add(
          tf.keras.layers.Dense(4, activation="relu", input_shape=(4,))
      )
      input_signature = [tf.TensorSpec([2, 4], tf.float32, name="x")]
      onnx_model, _ = tf2onnx.convert.from_keras(
          model, input_signature, opset=13
      )
  
      save_path = tmp_path / "test_model.onnx"
      onnx.save(onnx_model, save_path)
      model = load_model(save_path)
  
      batch = np.zeros((2, 4), np.float32)
      predict(model, batch)
  
file_path: unisim/tests/backend/test_onnx.py
imports: import numpy
  import onnx
  import tensorflow
  import tf2onnx
  from numpy.testing import assert_almost_equal
  from unisim.backend.onnx import cosine_similarity, load_model, predict
label_desc: Use image node_id 3 for calling a global function with example usage: test_onnx_predict(tmp_path) without return types