def __call__(self, output, frame_size, confidence_thresh=None):
        """Parses the model output.

        Args:
            output: a batch of predictions ``output = List[Dict[Tensor]]``,
                where each dict has the following keys:

                -   boxes (``FloatTensor[N, 4]``): the predicted boxes in
                    ``[x1, y1, x2, y2]`` format (absolute coordinates)
                -   labels (``Int64Tensor[N]``): the predicted labels
                -   scores (``Tensor[N]``): the scores for each prediction
                -   keypoints (``FloatTensor[N, K, ...]``): the predicted
                    keypoints for each instance in ``[x, y, v, ...]`` format

            frame_size: the ``(width, height)`` of the frames in the batch
            confidence_thresh (None): an optional confidence threshold to use
                to filter any applicable predictions

        Returns:
            a list of :class:`fiftyone.core.labels.Label` dicts
        """
        return [
            self._parse_output(o, frame_size, confidence_thresh)
            for o in output
        ]
Parses the model output.

        Args:
            output: a batch of predictions ``output = List[Dict[Tensor]]``,
                where each dict has the following keys:

                -   boxes (``FloatTensor[N, 4]``): the predicted boxes in
                    ``[x1, y1, x2, y2]`` format (absolute coordinates)
                -   labels (``Int64Tensor[N]``): the predicted labels
                -   scores (``Tensor[N]``): the scores for each prediction
                -   keypoints (``FloatTensor[N, K, ...]``): the predicted
                    keypoints for each instance in ``[x, y, v, ...]`` format

            frame_size: the ``(width, height)`` of the frames in the batch
            confidence_thresh (None): an optional confidence threshold to use
                to filter any applicable predictions

        Returns:
            a list of :class:`fiftyone.core.labels.Label` dicts
        
fiftyone/utils/torch.py