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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 fit(self, optimizer='RMSProp', iterations=1000, print_progress=True, start_diffuse=False, **kwargs):
 """ 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 """
     

        return self._bbvi_fit(optimizer=optimizer, print_progress=print_progress,
            start_diffuse=start_diffuse, iterations=iterations, **kwargs)