code: def format_for_prediction(self, report):
      # gather detection output keys across processes
      pred_boxes = gather_tensor_along_batch(report.pred_boxes)
      pred_logits = gather_tensor_along_batch(report.pred_logits)
      orig_size = gather_tensor_along_batch(report.orig_size)
  
      outputs = {"pred_logits": pred_logits, "pred_boxes": pred_boxes}
      if hasattr(report, "attr_logits"):
          attr_logits = gather_tensor_along_batch(report.attr_logits)
          outputs["attr_logits"] = attr_logits
  
      image_ids = report.image_id.tolist()
      results = self.postprocessors["bbox"](outputs, orig_size)
  
      predictions = []
      for image_id, r in zip(image_ids, results):
          scores = r["scores"].tolist()
          labels = r["labels"].tolist()
          # convert boxes from xyxy to xywh
          xmin, ymin, xmax, ymax = r["boxes"].unbind(1)
          boxes_xywh = torch.stack(
              (xmin, ymin, xmax - xmin, ymax - ymin), dim=1
          )
          boxes_xywh = boxes_xywh.tolist()
  
          # group the boxes by image_id for de-duplication in `on_prediction_end`
          # (duplication is introduced by DistributedSampler)
          predictions.append(
              (
                  image_id,
                  [
                      {
                          "image_id": image_id,
                          "category_id": labels[k],
                          "bbox": box_xywh,
                          "score": scores[k],
                      }
                      for k, box_xywh in enumerate(boxes_xywh)
                  ],
              )
          )
          if "attr_scores" in r:
              attr_scores = r["attr_scores"].tolist()
              attr_labels = r["attr_labels"].tolist()
              for k in range(len(boxes_xywh)):
                  predictions[-1][1][k]["attr_score"] = attr_scores[k]
                  predictions[-1][1][k]["attr_label"] = attr_labels[k]
  
      return predictions
  
file_path: mmf/mmf/datasets/builders/coco/detection_dataset.py
imports: import os
  from typing import Dict
  import torch
  import torch.nn.functional
  import torchvision
  from mmf.common.sample import Sample
  from mmf.datasets.base_dataset import BaseDataset
  from mmf.utils.distributed import gather_tensor_along_batch, object_to_byte_tensor
  from torch import nn, Tensor
label_desc: Use image node_id 4 for calling the DetectionCOCODataset obj's underlying member method code with example usage: obj.format_for_prediction(report) and returns: predictions