code: def get_detection_dataset_dicts(
      names,
      filter_empty=True,
      min_keypoints=0,
      proposal_files=None,
      check_consistency=True,
  ):
      """
      Load and prepare dataset dicts for instance detection/segmentation and semantic segmentation.
  
      Args:
          names (str or list[str]): a dataset name or 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 `names`.
          check_consistency (bool): whether to check if datasets have consistent metadata.
  
      Returns:
          list[dict]: a list of dicts following the standard dataset dict format.
      """
      if isinstance(names, str):
          names = [names]
      assert len(names), names
  
      available_datasets = DatasetCatalog.keys()
      names_set = set(names)
      if not names_set.issubset(available_datasets):
          logger = logging.getLogger(__name__)
          logger.warning(
              "The following dataset names are not registered in the DatasetCatalog: "
              f"{names_set - available_datasets}. "
              f"Available datasets are {available_datasets}"
          )
  
      dataset_dicts = [
          DatasetCatalog.get(dataset_name) for dataset_name in names
      ]
  
      if isinstance(dataset_dicts[0], torchdata.Dataset):
          if len(dataset_dicts) > 1:
              # ConcatDataset does not work for iterable style dataset.
              # We could support concat for iterable as well, but it's often
              # not a good idea to concat iterables anyway.
              return torchdata.ConcatDataset(dataset_dicts)
          return dataset_dicts[0]
  
      for dataset_name, dicts in zip(names, dataset_dicts):
          assert len(dicts), "Dataset '{}' is empty!".format(
              dataset_name
          )
  
      if proposal_files is not None:
          assert len(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 check_consistency and has_instances:
          try:
              class_names = MetadataCatalog.get(names[0]).thing_classes
              check_metadata_consistency("thing_classes", names)
              print_instances_class_histogram(
                  dataset_dicts, class_names
              )
          except (
              AttributeError
          ):  # class names are not available for this dataset
              pass
  
      assert len(dataset_dicts), "No valid data found in {}.".format(
          ",".join(names)
      )
      return dataset_dicts
  
imports: import itertools
  import logging
  import numpy
  import operator
  import pickle
  from collections import OrderedDict, defaultdict
  from typing import Any, Callable, Dict, List, Optional, Union
  import torch
  import torch.utils.data
  from tabulate import tabulate
  from termcolor import colored
  from detectron2.config import configurable
  from detectron2.structures import BoxMode
  from detectron2.utils.comm import get_world_size
  from detectron2.utils.env import seed_all_rng
  from detectron2.utils.file_io import PathManager
  from detectron2.utils.logger import _log_api_usage, log_first_n
  from .catalog import DatasetCatalog, MetadataCatalog
  from .common import AspectRatioGroupedDataset, DatasetFromList, MapDataset, ToIterableDataset
  from .dataset_mapper import DatasetMapper
  from .detection_utils import check_metadata_consistency
  from .samplers import InferenceSampler, RandomSubsetTrainingSampler, RepeatFactorTrainingSampler, TrainingSampler
label_desc: Use image node_id 5 for calling a global function with example usage: get_detection_dataset_dicts(names, filter_empty, min_keypoints, proposal_files, check_consistency) and returns: dataset_dicts, dataset_dicts, torchdata