Reformat with black

scripts/demo/streamlit_helpers.py

@@ -50,12 +50,12 @@ def __call__(self, image: torch.Tensor):
         n = image.shape[0]
         image_np = rearrange(
             (255 * image).detach().cpu(), "n b c h w -> (n b) h w c"
-        ).numpy()[:,:,:,::-1]
+        ).numpy()[:, :, :, ::-1]
         # torch (b, c, h, w) in [0, 1] -> numpy (b, h, w, c) [0, 255]
         for k in range(image_np.shape[0]):
             image_np[k] = self.encoder.encode(image_np[k], "dwtDct")
         image = torch.from_numpy(
-            rearrange(image_np[:,:,:,::-1], "(n b) h w c -> n b c h w", n=n)
+            rearrange(image_np[:, :, :, ::-1], "(n b) h w c -> n b c h w", n=n)
         ).to(image.device)
         image = torch.clamp(image / 255, min=0.0, max=1.0)
         if squeeze: