code: def test_default_Cnn1DDenoiser_sequential(rng=None):
      # Create dummy data
      normal_img = rng.random((10, 10, 10, 30))
      nos_img = normal_img + rng.normal(
          loc=0.0, scale=0.1, size=normal_img.shape
      )
      x = rng.random((10, 10, 10, 30))
      # Test 1D denoiser
      model = Cnn1DDenoiser(30)
      model.compile(
          optimizer="adam",
          loss="mean_squared_error",
          metrics=["accuracy"],
      )
      epochs = 1
      hist = model.fit(nos_img, normal_img, epochs=epochs)
      data = model.predict(x)
      model.evaluate(nos_img, normal_img, verbose=2)
      _ = hist.history["accuracy"][0]
      assert data.shape == x.shape
  
imports: import pytest
  from dipy.utils.optpkg import optional_package
  from dipy.testing.decorators import set_random_number_generator
label_desc: Use image node_id 1 for calling a global function with example usage: test_default_Cnn1DDenoiser_sequential(rng) without return types