    def __call__(self, vmasks, modality_masking=None, vfeats=None):
        """
        We perform lazy masking to save data transfer time.
        It only generates video_labels by default and MFM model
        will do actualy masking.
        Return: `video_label` is a binary mask.
        """
        video_label = vmasks.clone()
        if modality_masking is not None:
            if modality_masking == "full":
                probability_matrix = torch.full(video_label.shape, 1.)
            elif modality_masking == "no":
                probability_matrix = torch.full(video_label.shape, 0.)
            elif modality_masking == "inverse":
                probability_matrix = torch.full(
                    video_label.shape, 1. - self.mfm_probability)
            else:
                raise ValueError("unknown modality masking.", modality_masking)
        else:
            probability_matrix = torch.full(
                video_label.shape, self.mfm_probability)
        masked_indices = torch.bernoulli(probability_matrix).bool()
        # We only compute loss on masked tokens
        video_label[~masked_indices] = 0
        if vfeats is not None:
            vfeats[video_label, :] = 0.0
        return video_label
We perform lazy masking to save data transfer time.
It only generates video_labels by default and MFM model
will do actualy masking.
Return: `video_label` is a binary mask.
examples/MMPT/mmpt/processors/how2processor.py