code: def generate_multilabel_prediction(report):
      scores = torch.sigmoid(report.scores)
      return [
          {"id": image_id.item(), "scores": scores[idx].tolist()}
          for idx, image_id in enumerate(report.id)
      ]
  
imports: import copy
  import os
  import numpy
  import omegaconf
  import torch
  from mmf.common.sample import Sample
  from mmf.datasets.mmf_dataset import MMFDataset
  from mmf.utils.general import get_mmf_root
  from mmf.utils.visualize import visualize_images
  from PIL import Image
  from torchvision import transforms
label_desc: Use image node_id 2 for calling a global function with example usage: generate_multilabel_prediction(report) and returns: unknown