code: def get_detection_dataset_dicts(
      dataset_names,
      filter_empty=True,
      min_keypoints=0,
      proposal_files=None,
  ):
      """
      Load and prepare dataset dicts for instance detection/segmentation and semantic segmentation.
  
      Args:
          dataset_names (list[str]): a list of dataset names
          filter_empty (bool): whether to filter out images without instance annotations
          min_keypoints (int): filter out images with fewer keypoints than
              `min_keypoints`. Set to 0 to do nothing.
          proposal_files (list[str]): if given, a list of object proposal files
              that match each dataset in `dataset_names`.
  
      Returns:
          list[dict]: a list of dicts following the standard dataset dict format.
      """
      assert len(dataset_names)
      dataset_dicts = [
          DatasetCatalog.get(dataset_name)
          for dataset_name in dataset_names
      ]
      for dataset_name, dicts in zip(dataset_names, dataset_dicts):
          assert len(dicts), "Dataset '{}' is empty!".format(
              dataset_name
          )
  
      if proposal_files is not None:
          assert len(dataset_names) == len(proposal_files)
          # load precomputed proposals from proposal files
          dataset_dicts = [
              load_proposals_into_dataset(
                  dataset_i_dicts, proposal_file
              )
              for dataset_i_dicts, proposal_file in zip(
                  dataset_dicts, proposal_files
              )
          ]
  
      dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts))
  
      has_instances = "annotations" in dataset_dicts[0]
      if filter_empty and has_instances:
          dataset_dicts = filter_images_with_only_crowd_annotations(
              dataset_dicts
          )
      if min_keypoints > 0 and has_instances:
          dataset_dicts = filter_images_with_few_keypoints(
              dataset_dicts, min_keypoints
          )
  
      if has_instances:
          try:
              class_names = MetadataCatalog.get(
                  dataset_names[0]
              ).thing_classes
              check_metadata_consistency("thing_classes", dataset_names)
              print_instances_class_histogram(
                  dataset_dicts, class_names
              )
          except (
              AttributeError
          ):  # class names are not available for this dataset
              pass
      return dataset_dicts
  
imports: import itertools
  import random
  import logging
  import operator
  import dill
  import pickle
  from pprint import pformat
  from typing import List, Optional, Union
  import numpy
  import torch
  import torch.utils.data
  from ..structures import BoxMode
  from ...utils.comm import get_world_size
  from ...utils.random import seed_all_rng
  from ..transforms import instance_transforms
  from ..registry.catalog import DatasetCatalog, MetadataCatalog
  from .detection_utils import check_metadata_consistency
  from .None import detection_utils
  from .samplers import InferenceSampler, RepeatFactorTrainingSampler, TrainingSampler
label_desc: Use image node_id 5 for calling a global function with example usage: get_detection_dataset_dicts(dataset_names, filter_empty, min_keypoints, proposal_files) and returns: dataset_dicts