code: def test_dense(backend, io_type):
      model = tf.keras.models.Sequential()
      model.add(
          Dense(
              2,
              input_shape=(1,),
              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,
          )
      )
      model.add(Activation(activation="elu", name="Activation"))
      model.compile(optimizer="adam", loss="mse")
  
      X_input = np.random.rand(100, 1)
  
      keras_prediction = model.predict(X_input)
  
      config = hls4ml.utils.config_from_keras_model(model)
      output_dir = str(
          test_root_path
          / f"hls4mlprj_keras_api_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)
  
      np.testing.assert_allclose(
          hls_prediction, keras_prediction, rtol=1e-2, atol=0.01
      )
  
      assert len(model.layers) + 1 == len(hls_model.get_layers())
      assert (
          list(hls_model.get_layers())[0].attributes["class_name"]
          == "InputLayer"
      )
      assert (
          list(hls_model.get_layers())[1].attributes["class_name"]
          == model.layers[0]._name
      )
      assert (
          list(hls_model.get_layers())[2].attributes["class_name"]
          == model.layers[1]._name
      )
      assert list(hls_model.get_layers())[0].attributes[
          "input_shape"
      ] == list(model.layers[0].input_shape[1:])
      assert (
          list(hls_model.get_layers())[1].attributes["n_in"]
          == model.layers[0].input_shape[1:][0]
      )
      assert (
          list(hls_model.get_layers())[1].attributes["n_out"]
          == model.layers[0].output_shape[1:][0]
      )
      assert (
          list(hls_model.get_layers())[2].attributes["activation"]
          == str(model.layers[1].activation).split()[1]
      )
      assert (
          list(hls_model.get_layers())[1].attributes["activation"]
          == str(model.layers[0].activation).split()[1]
      )
  
file_path: hls4ml/test/pytest/test_keras_api.py
imports: import math
  from pathlib import Path
  import numpy
  import pytest
  import tensorflow
  from tensorflow.keras.layers import ELU, Activation, AveragePooling1D, AveragePooling2D, Conv1D, Conv2D, Dense, DepthwiseConv1D, DepthwiseConv2D, LeakyReLU, MaxPooling1D, MaxPooling2D, PReLU
  import hls4ml
label_desc: Use image node_id 1 for calling a global function with example usage: test_dense(backend, io_type) without return types