code: def contributions_from_model_image_and_galaxy_image(self, model_image, galaxy_image, minimum_value=0.0):
          
          contributions = np.divide(galaxy_image, np.add(model_image, self.contribution_factor))
          contributions = np.divide(contributions, np.max(contributions))
          contributions[contributions < minimum_value] = 0.0
          return contributions
docs: Compute the contribution map of a galaxy, which represents the fraction of flux in each pixel that the \
          galaxy is attributed to contain, scaled to the *contribution_factor* hyper-parameter.
  
          This is computed by dividing that galaxy's flux by the total flux in that pixel, and then scaling by the \
          maximum flux such that the contribution map ranges between 0 and 1.
  
          Parameters
          -----------
          model_image : ndarray
              The best-fit model image to the observed image from a previous analysis phase. This provides the \
              total light attributed to each image pixel by the model.
          galaxy_image : ndarray
              A model image of the galaxy (from light profiles or an inversion) from a previous analysis phase.
          minimum_value : float
              The minimum contribution value a pixel must contain to not be rounded to 0.