code: def test_multi_dense(backend, io_type):
      model = tf.keras.models.Sequential()
      model.add(
          Dense(
              4,
              input_shape=(
                  8,
                  8,
              ),
              name="Dense",
              use_bias=True,
              kernel_initializer=tf.keras.initializers.RandomUniform(
                  minval=1, maxval=10
              ),
              bias_initializer="zeros",
              kernel_regularizer=None,
              bias_regularizer=None,
              activity_regularizer=None,
              kernel_constraint=None,
              bias_constraint=None,
              activation="relu",
          )
      )
      model.compile(optimizer="adam", loss="mse")
  
      X_input = np.random.rand(100, 8, 8)
  
      keras_prediction = model.predict(X_input)
  
      default_precision = (
          "ap_fixed<32, 16>"
          if backend in ["Vivado", "Vitis"]
          else "ac_fixed<32, 16, true>"
      )
      config = hls4ml.utils.config_from_keras_model(
          model, default_precision=default_precision
      )
      output_dir = str(
          test_root_path / f"hls4mlprj_multi_dense_{backend}_{io_type}"
      )
  
      hls_model = hls4ml.converters.convert_from_keras_model(
          model,
          hls_config=config,
          output_dir=output_dir,
          backend=backend,
          io_type=io_type,
      )
  
      hls_model.compile()
  
      hls_prediction = hls_model.predict(X_input).reshape(
          keras_prediction.shape
      )
  
      np.testing.assert_allclose(
          hls_prediction, keras_prediction, rtol=1e-2, atol=0.01
      )
  
      assert (
          list(hls_model.get_layers())[1].class_name
          == "PointwiseConv1D"
      )
  
imports: from pathlib import Path
  import numpy
  import pytest
  import tensorflow
  from tensorflow.keras.layers import Dense
  import hls4ml
label_desc: Use image node_id 1 for calling a global function with example usage: test_multi_dense(backend, io_type) without return types