code: def test_softsign(backend, input_shape, io_type):
      X = np.random.rand(1000, *input_shape)
      model = tf.keras.models.Sequential()
      model.add(
          tf.keras.layers.Activation(
              input_shape=input_shape,
              activation="softsign",
              name="softsign",
          )
      )
      model.compile()
  
      cfg = hls4ml.utils.config_from_keras_model(
          model, granularity="name"
      )
      odir = str(
          test_root_path
          / f"hls4mlprj_softsign_{backend}_{io_type}_{str(input_shape)}"
      )
      hls_model = hls4ml.converters.convert_from_keras_model(
          model,
          hls_config=cfg,
          io_type=io_type,
          output_dir=odir,
          backend=backend,
      )
      hls_model.compile()
  
      y_keras = model.predict(X)
      y_hls4ml = hls_model.predict(X).reshape(y_keras.shape)
      acc_hls4ml = accuracy_score(
          np.argmax(y_keras, axis=-1).ravel(),
          np.argmax(y_hls4ml, axis=-1).ravel(),
      )
  
      print(f"Accuracy hls4ml relative to keras: {acc_hls4ml}")
      assert acc_hls4ml >= 0.96
  
imports: from pathlib import Path
  import numpy
  import pytest
  import tensorflow
  from sklearn.metrics import accuracy_score
  import hls4ml
label_desc: Use image node_id 1 for calling a global function with example usage: test_softsign(backend, input_shape, io_type) without return types