class_docstr: A callable which takes a dataset dict in Detectron2 Dataset format,
  and map it into a format used by the model.
  
  This is the default callable to be used to map your dataset dict into training data.
  You may need to follow it to implement your own one for customized logic,
  such as a different way to read or transform images.
  See :doc:`/tutorials/data_loading` for details.
  
  The callable currently does the following:
  
  1. Read the image from "file_name"
  2. Applies cropping/geometric transforms to the image and annotations
  3. Prepare data and annotations to Tensor and :class:`Instances`
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docstr: Args:
      dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
  
  Returns:
      dict: a format that builtin models in detectron2 accept
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code: def __call__(self, dataset_dict):
      """
      Args:
          dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
  
      Returns:
          dict: a format that builtin models in detectron2 accept
      """
      dataset_dict = copy.deepcopy(
          dataset_dict
      )  # it will be modified by code below
      # USER: Write your own image loading if it's not from a file
      image = utils.read_image(
          dataset_dict["file_name"], format=self.image_format
      )
      utils.check_image_size(dataset_dict, image)
  
      # USER: Remove if you don't do semantic/panoptic segmentation.
      if "sem_seg_file_name" in dataset_dict:
          sem_seg_gt = utils.read_image(
              dataset_dict.pop("sem_seg_file_name"), "L"
          ).squeeze(2)
      else:
          sem_seg_gt = None
  
      aug_input = T.StandardAugInput(image, sem_seg=sem_seg_gt)
      transforms = aug_input.apply_augmentations(self.augmentations)
      image, sem_seg_gt = aug_input.image, aug_input.sem_seg
  
      image_shape = image.shape[:2]  # h, w
      # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
      # but not efficient on large generic data structures due to the use of pickle & mp.Queue.
      # Therefore it's important to use torch.Tensor.
      dataset_dict["image"] = torch.as_tensor(
          np.ascontiguousarray(image.transpose(2, 0, 1))
      )
      if sem_seg_gt is not None:
          dataset_dict["sem_seg"] = torch.as_tensor(
              sem_seg_gt.astype("long")
          )
  
      # USER: Remove if you don't use pre-computed proposals.
      # Most users would not need this feature.
      if self.proposal_topk is not None:
          utils.transform_proposals(
              dataset_dict,
              image_shape,
              transforms,
              proposal_topk=self.proposal_topk,
          )
  
      if not self.is_train:
          # USER: Modify this if you want to keep them for some reason.
          dataset_dict.pop("annotations", None)
          dataset_dict.pop("sem_seg_file_name", None)
          return dataset_dict
  
      if "annotations" in dataset_dict:
          # USER: Modify this if you want to keep them for some reason.
          for anno in dataset_dict["annotations"]:
              if not self.use_instance_mask:
                  anno.pop("segmentation", None)
              if not self.use_keypoint:
                  anno.pop("keypoints", None)
  
          # USER: Implement additional transformations if you have other types of data
          annos = [
              utils.transform_instance_annotations(
                  obj,
                  transforms,
                  image_shape,
                  keypoint_hflip_indices=self.keypoint_hflip_indices,
              )
              for obj in dataset_dict.pop("annotations")
              if obj.get("iscrowd", 0) == 0
          ]
          instances = utils.annotations_to_instances(
              annos, image_shape, mask_format=self.instance_mask_format
          )
  
          # After transforms such as cropping are applied, the bounding box may no longer
          # tightly bound the object. As an example, imagine a triangle object
          # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight
          # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to
          # the intersection of original bounding box and the cropping box.
          if self.recompute_boxes:
              instances.gt_boxes = (
                  instances.gt_masks.get_bounding_boxes()
              )
          dataset_dict["instances"] = utils.filter_empty_instances(
              instances
          )
      return dataset_dict
  
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 3 for calling the DatasetMapper obj's underlying member method code with example usage: obj.__call__(dataset_dict) and returns: dataset_dict, dataset_dict