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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def gaussian_noise(x, severity=1):
 """Gaussian noise corruption to images. Args: x: numpy array, uncorrupted image, assumed to have uint8 pixel in [0,255]. severity: integer, severity of corruption. Returns: numpy array, image with uint8 pixels in [0,255]. Added Gaussian noise. """

  c = [.08, .12, 0.18, 0.26, 0.38][severity - 1]
  x = np.array(x) / 255.
  x_clip = np.clip(x + np.random.normal(size=x.shape, scale=c), 0, 1) * 255
  return around_and_astype(x_clip)