code: def test_causalpadding(io_type, backend):
      model = Sequential()
      model.add(Conv1D(1, 5, padding="causal", input_shape=(100, 1)))
      model.compile()
  
      data = np.random.randint(0, 10, 100).astype(float)
      data = np.expand_dims(data, axis=0)
      data = np.expand_dims(data, axis=-1)
  
      config = hls4ml.utils.config_from_keras_model(
          model, default_precision="ap_fixed<32,16>", granularity="name"
      )
      odir = str(
          test_root_path / f"hls4mlprj_validpadding_{backend}_{io_type}"
      )
      hls_model = hls4ml.converters.convert_from_keras_model(
          model,
          hls_config=config,
          io_type=io_type,
          output_dir=odir,
          backend=backend,
      )
      hls_model.compile()
  
      # Predict
      y_keras = model.predict(data).flatten()
      y_hls = hls_model.predict(data).flatten()
      np.testing.assert_allclose(
          y_keras, y_hls, rtol=0, atol=atol, verbose=True
      )
  
imports: from pathlib import Path
  import numpy
  import pytest
  from tensorflow.keras.layers import Conv1D
  from tensorflow.keras.models import Sequential
  import hls4ml
label_desc: Use image node_id 1 for calling a global function with example usage: test_causalpadding(io_type, backend) without return types