code: def fit(self, optimizer='RMSProp', iterations=1000, print_progress=True, start_diffuse=False, **kwargs):
               
  
          return self._bbvi_fit(optimizer=optimizer, print_progress=print_progress,
              start_diffuse=start_diffuse, iterations=iterations, **kwargs)
docs: Fits the model
  
          Parameters
          ----------
          optimizer : string
              Stochastic optimizer: either RMSProp or ADAM.
  
          iterations: int
              How many iterations to run
  
          print_progress : bool
              Whether tp print the ELBO progress or not
          
          start_diffuse : bool
              Whether to start from diffuse values (if not: use approx Gaussian)
          
          Returns
          ----------
          BBVI fit object