How to use the implicit._als function in implicit

solver = self.solver

        log.debug("Running %i ALS iterations", self.iterations)
        with tqdm(total=self.iterations, disable=not show_progress) as progress:
            # alternate between learning the user_factors from the item_factors and vice-versa
            for iteration in range(self.iterations):
                s = time.time()
                solver(Cui, self.user_factors, self.item_factors, self.regularization,
                       num_threads=self.num_threads)
                solver(Ciu, self.item_factors, self.user_factors, self.regularization,
                       num_threads=self.num_threads)
                progress.update(1)

                if self.calculate_training_loss:
                    loss = _als.calculate_loss(Cui, self.user_factors, self.item_factors,
                                               self.regularization, num_threads=self.num_threads)
                    progress.set_postfix({"loss": loss})

                if self.fit_callback:
                    self.fit_callback(iteration, time.time() - s)

        if self.calculate_training_loss:
            log.info("Final training loss %.4f", loss)

def solver(self):
        if self.use_cg:
            solver = _als.least_squares_cg if self.use_native else least_squares_cg
            return functools.partial(solver, cg_steps=self.cg_steps)
        return _als.least_squares if self.use_native else least_squares