code: def test_activations(backend, activation, name, shape, io_type):
      # Subtract 0.5 to include negative values
      X = np.random.rand(1000, *shape) - 0.5
  
      input = Input(shape=shape)
      activation = activation(input)
      keras_model = Model(inputs=input, outputs=activation)
  
      hls_config = hls4ml.utils.config_from_keras_model(keras_model)
      output_dir = str(
          test_root_path / "hls4mlprj_activations_{}_{}_{}_{}"
      ).format(backend, io_type, str(shape), name)
  
      hls_model = hls4ml.converters.convert_from_keras_model(
          keras_model,
          hls_config=hls_config,
          io_type=io_type,
          output_dir=output_dir,
          backend=backend,
      )
      hls_model.compile()
  
      keras_prediction = keras_model.predict(X)
      hls_prediction = hls_model.predict(X).reshape(
          keras_prediction.shape
      )
  
      np.testing.assert_allclose(
          hls_prediction, keras_prediction, rtol=2e-2, atol=2e-2
      )
  
imports: from pathlib import Path
  import numpy
  import pytest
  from tensorflow.keras.layers import ELU, Activation, Input, LeakyReLU, ReLU, ThresholdedReLU
  from tensorflow.keras.models import Model
  import hls4ml
label_desc: Use image node_id 1 for calling a global function with example usage: test_activations(backend, activation, name, shape, io_type) without return types