<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def fit(self, item_users, show_progress=True):
 """ Factorizes the item_users matrix. After calling this method, the members 'user_factors' and 'item_factors' will be initialized with a latent factor model of the input data. The item_users matrix does double duty here. It defines which items are liked by which users (P_iu in the original paper), as well as how much confidence we have that the user liked the item (C_iu). The negative items are implicitly defined: This code assumes that non-zero items in the item_users matrix means that the user liked the item. The negatives are left unset in this sparse matrix: the library will assume that means Piu = 0 and Ciu = 1 for all these items. Parameters item_users: csr_matrix Matrix of confidences for the liked items. This matrix should be a csr_matrix where the rows of the matrix are the item, the columns are the users that liked that item, and the value is the confidence that the user liked the item. show_progress : bool, optional Whether to show a progress bar during fitting """

        Ciu = item_users
        if not isinstance(Ciu, scipy.sparse.csr_matrix):
            s = time.time()
            log.debug("Converting input to CSR format")
            Ciu = Ciu.tocsr()
            log.debug("Converted input to CSR in %.3fs", time.time() - s)

        if Ciu.dtype != np.float32:
            Ciu = Ciu.astype(np.float32)

        s = time.time()
        Cui = Ciu.T.tocsr()
        log.debug("Calculated transpose in %.3fs", time.time() - s)

        items, users = Ciu.shape

        s = time.time()
        # Initialize the variables randomly if they haven't already been set
        if self.user_factors is None:
            self.user_factors = np.random.rand(users, self.factors).astype(self.dtype) * 0.01
        if self.item_factors is None:
            self.item_factors = np.random.rand(items, self.factors).astype(self.dtype) * 0.01

        log.debug("Initialized factors in %s", time.time() - s)

        # invalidate cached norms and squared factors
        self._item_norms = None
        self._YtY = None

        if self.use_gpu:
            return self._fit_gpu(Ciu, Cui, show_progress)

        solver = self.solver

        log.debug("Running %i ALS iterations", self.iterations)
        with tqdm.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)
                progress.update(.5)

                solver(Ciu, self.item_factors, self.user_factors, self.regularization,
                       num_threads=self.num_threads)
                progress.update(.5)

                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)