code: def mse(a: np.array, b: np.array, **kwargs) -> float:
      """
      Calculate Mean Squared Error between two images (or explanations).
  
      Parameters
      ----------
      a: np.ndarray
               Array to calculate MSE with.
      b: np.ndarray
               Array to calculate MSE with.
      kwargs: optional
              Keyword arguments.
          normalise_mse: boolean
              Indicates whether to returned a normalised MSE calculation or not.
  
      Returns
      -------
      float:
          A floating point of MSE.
      """
  
      normalise = kwargs.get("normalise_mse", False)
  
      if normalise:
          # Calculate MSE in its polynomial expansion (a-b)^2 = a^2 - 2ab + b^2.
          return np.average(
              ((a**2) - (2 * (a * b)) + (b**2)), axis=0
          )
      # If no need to normalise, return (a-b)^2.
  
      return np.average(((a - b) ** 2), axis=0)
  
label_desc: Use image node_id 1 for calling a global function with example usage: mse(a, b) and returns: np, np