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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 _get_scale_and_shape_sim(self, transformed_lvs):
 """ Obtains model scale, shape, skewness latent variables for a 2d array of simulations. Parameters transformed_lvs : np.array Transformed latent variable vector (2d - with draws of each variable) Returns - Tuple of np.arrays (each being scale, shape and skewness draws) """


        if self.scale is True:
            if self.shape is True:
                model_shape = self.latent_variables.z_list[-1].prior.transform(transformed_lvs[-1, :]) 
                model_scale = self.latent_variables.z_list[-2].prior.transform(transformed_lvs[-2, :])
            else:
                model_shape = np.zeros(transformed_lvs.shape[1])
                model_scale = self.latent_variables.z_list[-1].prior.transform(transformed_lvs[-1, :])
        else:
            model_scale = np.zeros(transformed_lvs.shape[1])
            model_shape = np.zeros(transformed_lvs.shape[1])

        if self.skewness is True:
            model_skewness = self.latent_variables.z_list[-3].prior.transform(transformed_lvs[-3, :])
        else:
            model_skewness = np.zeros(transformed_lvs.shape[1])

        return model_scale, model_shape, model_skewness