nit

Author: NicolasHug

torchvision/io/video.py

@@ -63,11 +63,11 @@ def write_video(
 ) -> None:
     """
     Writes a 4d tensor in [T, H, W, C] format in a video file.
-    The default parameters (i.e. `fps`, `audio_fps`) return videos
-    of a fixed quality & compressing speed, and may not necessarily be suitable for all applications.
-    Since torchvision relies on `PyAV` (therefore, ultimately `FFmpeg`) to encode videos, 
-    you can get more fine-grained control by referring to the other options at 
-    your disposal within `the FFMpeg wiki <http://trac.ffmpeg.org/wiki#Encoding>`_.
+
+    This function relies on PyAV (therefore, ultimately FFmpeg) to encode
+    videos, you can get more fine-grained control by referring to the other
+    options at your disposal within `the FFMpeg wiki
+    <http://trac.ffmpeg.org/wiki#Encoding>`_.
 
     Args:
         filename (str): path where the video will be saved
@@ -85,15 +85,15 @@ def write_video(
         audio_options (Dict): dictionary containing options to be passed into the PyAV audio stream.
             The list of options is codec-dependent and can all
             be found from `the FFMpeg wiki <http://trac.ffmpeg.org/wiki#Encoding>`_.
-    
+
     Examples::
         >>> # Creating libx264 video with CRF 17, for visually lossless footage:
         >>>
         >>> from torchvision.io import write_video
         >>> # 1000 frames of 100x100, 3-channel image.
-        >>> vid = torch.randn(1000, 100, 100, 3, dtype = torch.uint8) 
+        >>> vid = torch.randn(1000, 100, 100, 3, dtype = torch.uint8)
         >>> write_video("video.mp4", options = {"crf": "17"})
-    
+
     """
     if not torch.jit.is_scripting() and not torch.jit.is_tracing():
         _log_api_usage_once(write_video)