code: def combine_detection_dataset_dicts(
      dataset_names: Collection[str],
      keep_instance_predicate: Optional[InstancePredicate] = None,
      proposal_files: Optional[Collection[str]] = None,
  ) -> List[Instance]:
      """
      Load and prepare dataset dicts for training / testing
  
      Args:
          dataset_names (Collection[str]): a list of dataset names
          keep_instance_predicate (Callable: Dict[str, Any] -> bool): predicate
              applied to instance dicts which defines whether to keep the instance
          proposal_files (Collection[str]): if given, a list of object proposal files
              that match each dataset in `dataset_names`.
      """
      assert len(dataset_names)
      if proposal_files is None:
          proposal_files = [None] * len(dataset_names)
      assert len(dataset_names) == len(proposal_files)
      # load datasets and metadata
      dataset_name_to_dicts = {}
      for dataset_name in dataset_names:
          dataset_name_to_dicts[dataset_name] = DatasetCatalog.get(
              dataset_name
          )
          assert len(
              dataset_name_to_dicts
          ), f"Dataset '{dataset_name}' is empty!"
      # merge categories, requires category metadata to be loaded
      # cat_id -> [(orig_cat_id, cat_name, dataset_name)]
      merged_categories = _merge_categories(dataset_names)
      _warn_if_merged_different_categories(merged_categories)
      merged_category_names = [
          merged_categories[cat_id][0].mapped_name
          for cat_id in sorted(merged_categories)
      ]
      # map to contiguous category IDs
      _add_category_id_to_contiguous_id_maps_to_metadata(
          merged_categories
      )
      # load annotations and dataset metadata
      for dataset_name, proposal_file in zip(
          dataset_names, proposal_files
      ):
          dataset_dicts = dataset_name_to_dicts[dataset_name]
          assert len(
              dataset_dicts
          ), f"Dataset '{dataset_name}' is empty!"
          if proposal_file is not None:
              dataset_dicts = load_proposals_into_dataset(
                  dataset_dicts, proposal_file
              )
          dataset_dicts = _maybe_filter_and_map_categories(
              dataset_name, dataset_dicts
          )
          print_instances_class_histogram(
              dataset_dicts, merged_category_names
          )
          dataset_name_to_dicts[dataset_name] = dataset_dicts
  
      if keep_instance_predicate is not None:
          all_datasets_dicts_plain = [
              d
              for d in itertools.chain.from_iterable(
                  dataset_name_to_dicts.values()
              )
              if keep_instance_predicate(d)
          ]
      else:
          all_datasets_dicts_plain = list(
              itertools.chain.from_iterable(
                  dataset_name_to_dicts.values()
              )
          )
      return all_datasets_dicts_plain
  
imports: import itertools
  import logging
  import numpy
  from collections import UserDict, defaultdict
  from dataclasses import dataclass
  from typing import Any, Callable, Collection, Dict, Iterable, List, Optional, Sequence, Tuple
  import torch
  from torch.utils.data.dataset import Dataset
  from detectron2.config import CfgNode
  from detectron2.data.build import build_detection_test_loader
  from detectron2.data.build import build_detection_train_loader
  from detectron2.data.build import load_proposals_into_dataset, print_instances_class_histogram, trivial_batch_collator, worker_init_reset_seed
  from detectron2.data.catalog import DatasetCatalog, Metadata, MetadataCatalog
  from detectron2.data.samplers import TrainingSampler
  from detectron2.utils.comm import get_world_size
  from densepose.config import get_bootstrap_dataset_config
  from densepose.modeling import build_densepose_embedder
  from .combined_loader import CombinedDataLoader, Loader
  from .dataset_mapper import DatasetMapper
  from .datasets.coco import DENSEPOSE_CSE_KEYS_WITHOUT_MASK, DENSEPOSE_IUV_KEYS_WITHOUT_MASK
  from .datasets.dataset_type import DatasetType
  from .inference_based_loader import InferenceBasedLoader, ScoreBasedFilter
  from .samplers import DensePoseConfidenceBasedSampler, DensePoseCSEConfidenceBasedSampler, DensePoseCSEUniformSampler, DensePoseUniformSampler, MaskFromDensePoseSampler, PredictionToGroundTruthSampler
  from .transform import ImageResizeTransform
  from .utils import get_category_to_class_mapping, get_class_to_mesh_name_mapping
  from .video import FirstKFramesSelector, FrameSelectionStrategy, LastKFramesSelector, RandomKFramesSelector, VideoKeyframeDataset, video_list_from_file
label_desc: Use image node_id 18 for calling a global function with example usage: combine_detection_dataset_dicts(dataset_names, keep_instance_predicate, proposal_files) and returns: all_datasets_dicts_plain