def METHOD_NAME(self, clip_input: torch.FloatTensor, images: torch.FloatTensor):
    pooled_output = self.vision_model(clip_input)[1]  # pooled_output
    image_embeds = self.visual_projection(pooled_output)
    special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds)
    cos_dist = cosine_distance(image_embeds, self.concept_embeds)
    # increase this value to create a stronger `nsfw` filter
    # at the cost of increasing the possibility of filtering benign images
    adjustment = 0.0
    special_scores = special_cos_dist - self.special_care_embeds_weights + adjustment
    # special_scores = special_scores.round(decimals=3)
    special_care = torch.any(special_scores > 0, dim=1)
    special_adjustment = special_care * 0.01
    special_adjustment = special_adjustment.unsqueeze(1).expand(-1, cos_dist.shape[1])
    concept_scores = (cos_dist - self.concept_embeds_weights) + special_adjustment
    # concept_scores = concept_scores.round(decimals=3)
    has_nsfw_concepts = torch.any(concept_scores > 0, dim=1)
    images[has_nsfw_concepts] = 0.0  # black image
    return images, has_nsfw_concepts