code: def shannon_entropy(image, base=2):
      """Calculate the Shannon entropy of an image.
  
      The Shannon entropy is defined as S = -sum(pk * log(pk)),
      where pk are frequency/probability of pixels of value k.
  
      Parameters
      ----------
      image : (M, N) ndarray
          Grayscale input image.
      base : float, optional
          The logarithmic base to use.
  
      Returns
      -------
      entropy : float
  
      Notes
      -----
      The returned value is measured in bits or shannon (Sh) for base=2, natural
      unit (nat) for base=np.e and hartley (Hart) for base=10.
  
      References
      ----------
      .. [1] `https://en.wikipedia.org/wiki/Entropy_(information_theory) <https://en.wikipedia.org/wiki/Entropy_(information_theory)>`_
      .. [2] https://en.wiktionary.org/wiki/Shannon_entropy
  
      Examples
      --------
      >>> from skimage import data
      >>> from skimage.measure import shannon_entropy
      >>> shannon_entropy(data.camera())
      7.231695011055706
      """
  
      _, counts = unique(image, return_counts=True)
      return scipy_entropy(counts, base=base)
  
label_desc: Use image node_id 1 for calling a global function with example usage: shannon_entropy(image, base) and returns: scipy_entropy