docs: def _fit_gpu(self, Ciu_host, Cui_host, show_progress=True):
          """ specialized training on the gpu. copies inputs to/from cuda device """
          if not implicit.cuda.HAS_CUDA:
              raise ValueError("No CUDA extension has been built, can't train on GPU.")
  
          if self.dtype == np.float64:
              log.warning("Factors of dtype float64 aren't supported with gpu fitting. "
                          "Converting factors to float32")
              self.item_factors = self.item_factors.astype(np.float32)
              self.user_factors = self.user_factors.astype(np.float32)
  
          Ciu = implicit.cuda.CuCSRMatrix(Ciu_host)
          Cui = implicit.cuda.CuCSRMatrix(Cui_host)
          X = implicit.cuda.CuDenseMatrix(self.user_factors.astype(np.float32))
          Y = implicit.cuda.CuDenseMatrix(self.item_factors.astype(np.float32))
  
          solver = implicit.cuda.CuLeastSquaresSolver(self.factors)
          log.debug("Running %i ALS iterations", self.iterations)
          with tqdm.tqdm(total=self.iterations, disable=not show_progress) as progress:
              for iteration in range(self.iterations):
                  s = time.time()
                  solver.least_squares(Cui, X, Y, self.regularization, self.cg_steps)
                  progress.update(.5)
                  solver.least_squares(Ciu, Y, X, self.regularization, self.cg_steps)
                  progress.update(.5)
  
                  if self.fit_callback:
                      self.fit_callback(iteration, time.time() - s)
  
                  if self.calculate_training_loss:
                      loss = solver.calculate_loss(Cui, X, Y, self.regularization)
                      progress.set_postfix({"loss": loss})
  
          if self.calculate_training_loss:
              log.info("Final training loss %.4f", loss)
  
          X.to_host(self.user_factors)
          Y.to_host(self.item_factors)