Fix mypy issues

src/torchcodec/decoders/_core/_metadata.py

@@ -60,7 +60,7 @@ def best_video_stream(self) -> StreamMetadata:
         return self.streams[self.best_video_stream_index]
 
 
-def get_video_metadata(decoder: torch.tensor) -> VideoMetadata:
+def get_video_metadata(decoder: torch.Tensor) -> VideoMetadata:
 
     container_dict = json.loads(_get_container_json_metadata(decoder))
     streams_metadata = []

src/torchcodec/decoders/_core/video_decoder_ops.py

@@ -176,23 +176,23 @@ def get_frames_in_range_abstract(
 
 
 @impl_abstract("torchcodec_ns::get_json_metadata")
-def get_json_metadata_abstract(decoder: torch.Tensor) -> str:
-    return torch.empty_like("")
+def get_json_metadata_abstract(decoder: torch.Tensor) -> torch.Tensor:
+    return torch.empty_like("")  # type: ignore[arg-type]
 
 
 @impl_abstract("torchcodec_ns::get_container_json_metadata")
-def get_container_json_metadata_abstract(decoder: torch.Tensor) -> str:
-    return torch.empty_like("")
+def get_container_json_metadata_abstract(decoder: torch.Tensor) -> torch.Tensor:
+    return torch.empty_like("")  # type: ignore[arg-type]
 
 
 @impl_abstract("torchcodec_ns::get_stream_json_metadata")
-def get_stream_json_metadata_abstract(decoder: torch.Tensor, stream_idx: int) -> str:
-    return torch.empty_like("")
+def get_stream_json_metadata_abstract(decoder: torch.Tensor, stream_idx: int) -> torch.Tensor:
+    return torch.empty_like("")  # type: ignore[arg-type]
 
 
 @impl_abstract("torchcodec_ns::_get_json_ffmpeg_library_versions")
-def _get_json_ffmpeg_library_versions_abstract() -> str:
-    return torch.empty_like("")
+def _get_json_ffmpeg_library_versions_abstract() -> torch.Tensor:
+    return torch.empty_like("")  # type: ignore[arg-type]
 
 
 @impl_abstract("torchcodec_ns::scan_all_streams_to_update_metadata")

src/torchcodec/decoders/_simple_video_decoder.py

@@ -27,7 +27,7 @@ def __init__(self, source: Union[str, Path, bytes, torch.Tensor]):
         core.add_video_stream(self._decoder)
 
         self.stream_metadata = _get_and_validate_stream_metadata(self._decoder)
-        self._num_frames = self.stream_metadata.num_frames_computed
+        self._num_frames: int = self.stream_metadata.num_frames_computed  # type: ignore[assignment]
         self._stream_index = self.stream_metadata.stream_index
 
     def __len__(self) -> int:

src/torchcodec/samplers/video_clip_sampler.py

@@ -134,7 +134,7 @@ def __init__(
         self.sampler_args = sampler_args
         self.decorder_args = DecoderArgs() if decorder_args is None else decorder_args
 
-    def forward(self, video_data: Tensor) -> Union[List[List[Tensor]], List[Tensor]]:
+    def forward(self, video_data: Tensor) -> Union[List[Any]]:
         """Sample video clips from the video data
 
         Args:
@@ -162,8 +162,7 @@ def forward(self, video_data: Tensor) -> Union[List[List[Tensor]], List[Tensor]]
             num_threads=self.decorder_args.num_threads,
         )
 
-        clips = []
-
+        clips: List[Any] = []
         # Cast sampler args to be time based or index based
         if isinstance(self.sampler_args, TimeBasedSamplerArgs):
             time_based_sampler_args = self.sampler_args
@@ -218,7 +217,6 @@ def _get_clips_for_index_based_sampling(
         )
         sampler_type = index_based_sampler_args.sampler_type
 
-        clip_start_idxs = []
         if sampler_type == "random":
             clip_start_idxs = torch.randint(
                 sample_start_index,
@@ -252,7 +250,7 @@ def _get_start_seconds(
         self,
         metadata_json: Dict[str, Any],
         time_based_sampler_args: TimeBasedSamplerArgs,
-    ):
+    ) -> List[float]:
         """Get start seconds for each clip.
         Given different sampler type, the API returns different clip start seconds.