code: def __init__(
      self, config, dataset_type, imdb_file_index, *args, **kwargs
  ):
      if "name" in kwargs:
          name = kwargs["name"]
      elif "dataset_name" in kwargs:
          name = kwargs["dataset_name"]
      else:
          name = "detection_coco"
      super().__init__(name, config, dataset_type, *args, **kwargs)
      self.dataset_name = name
  
      image_dir = self.config.images[self._dataset_type][
          imdb_file_index
      ]
      self.image_dir = os.path.join(self.config.data_dir, image_dir)
      coco_json = self.config.annotations[self._dataset_type][
          imdb_file_index
      ]
      self.coco_json = os.path.join(self.config.data_dir, coco_json)
  
      self.coco_dataset = torchvision.datasets.CocoDetection(
          self.image_dir, self.coco_json
      )
  
      self.postprocessors = {"bbox": PostProcess()}
  
file_path: mmf/mmf/datasets/builders/coco/detection_dataset.py
imports: import os
  from typing import Dict
  import torch
  import torch.nn.functional
  import torchvision
  from mmf.common.sample import Sample
  from mmf.datasets.base_dataset import BaseDataset
  from mmf.utils.distributed import gather_tensor_along_batch, object_to_byte_tensor
  from torch import nn, Tensor
label_desc: Use image node_id 1 to create a new DetectionCOCODataset object from inherited base classes: BaseDataset with example: obj = DetectionCOCODataset(config, dataset_type, imdb_file_index)