code: def coco_ssd_collate_fn(
      batch: List[Mapping[str, Union[Tensor, Mapping[str, Tensor]]]],
      opts: argparse.Namespace,
  ) -> Mapping[str, Union[Tensor, Mapping[str, Tensor]]]:
      """Combines a list of dictionaries into a single dictionary by concatenating matching fields.
  
      For expected keys, see the keys in the output of `__getitem__` function of COCODetectionSSD class.
  
      Args:
          batch: A list of dictionaries
          opts: Command-line arguments
  
      Returns:
          A dictionary with `samples` and `targets` as keys.
      """
      new_batch = {
          "samples": {"image": []},
          "targets": {
              "box_labels": [],
              "box_coordinates": [],
              "image_id": [],
              "image_width": [],
              "image_height": [],
          },
      }
  
      for b_id, batch_ in enumerate(batch):
          # prepare inputs
          new_batch["samples"]["image"].append(
              batch_["samples"]["image"]
          )
  
          # prepare outputs
          new_batch["targets"]["box_labels"].append(
              batch_["targets"]["box_labels"]
          )
          new_batch["targets"]["box_coordinates"].append(
              batch_["targets"]["box_coordinates"]
          )
          new_batch["targets"]["image_id"].append(
              batch_["targets"]["image_id"]
          )
          new_batch["targets"]["image_width"].append(
              batch_["targets"]["image_width"]
          )
          new_batch["targets"]["image_height"].append(
              batch_["targets"]["image_height"]
          )
  
      # stack inputs
      new_batch["samples"]["image"] = torch.stack(
          new_batch["samples"]["image"], dim=0
      )
  
      # stack outputs
      new_batch["targets"]["box_labels"] = torch.stack(
          new_batch["targets"]["box_labels"], dim=0
      )
  
      new_batch["targets"]["box_coordinates"] = torch.stack(
          new_batch["targets"]["box_coordinates"], dim=0
      )
  
      new_batch["targets"]["image_id"] = torch.stack(
          new_batch["targets"]["image_id"], dim=0
      )
  
      new_batch["targets"]["image_width"] = torch.stack(
          new_batch["targets"]["image_width"], dim=0
      )
  
      new_batch["targets"]["image_height"] = torch.stack(
          new_batch["targets"]["image_height"], dim=0
      )
  
      return new_batch
  
imports: import argparse
  import math
  from typing import List, Mapping, Tuple, Union
  import torch
  from torch import Tensor
  from cvnets.anchor_generator import build_anchor_generator
  from cvnets.matcher_det import build_matcher
  from data.collate_fns import COLLATE_FN_REGISTRY
  from data.datasets import DATASET_REGISTRY
  from data.datasets.detection.coco_base import COCODetection
  from data.transforms import image_pil
  from data.transforms.common import Compose
  from utils import logger
label_desc: Use image node_id 1 for calling a global function with example usage: coco_ssd_collate_fn(batch, opts) and returns: new_batch