class_docstr: Support MMDET object detection models.
  Refer to https://github.com/open-mmlab/mmdetection
docstr: Parameters
  ----------
  batch
      A dictionary containing the input mini-batch data.
      We need to use the keys with the model prefix to index required data.
  mode
      "loss" or "predict". TODO: support "tensor"
      https://github.com/open-mmlab/mmdetection/blob/main/mmdet/models/detectors/base.py#L58C1
  
  Returns
  -------
      A dictionary with bounding boxes.
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code: def forward(
      self,
      batch,
      mode,
  ):
      """
      Parameters
      ----------
      batch
          A dictionary containing the input mini-batch data.
          We need to use the keys with the model prefix to index required data.
      mode
          "loss" or "predict". TODO: support "tensor"
          https://github.com/open-mmlab/mmdetection/blob/main/mmdet/models/detectors/base.py#L58C1
  
      Returns
      -------
          A dictionary with bounding boxes.
      """
  
      self.set_data_preprocessor_device()
      data = self.data_preprocessor(batch)
      rets = self.model(
          inputs=data["inputs"],
          data_samples=data["data_samples"],
          mode=mode,
      )
  
      if mode == "loss":
          return rets
      elif mode == "predict":
          # for detailed data structure, see https://github.com/open-mmlab/mmdetection/blob/main/mmdet/structures/det_data_sample.py
          return [
              {BBOX: ret.pred_instances, LABEL: ret.gt_instances}
              for ret in rets
          ]
      else:
          raise ValueError(f"{mode} mode is not supported.")
  
imports: import logging
  import os
  import time
  import warnings
  from typing import Optional
  import torch
  from torch import nn
  from ..constants import BBOX, BBOX_FORMATS, COLUMN, IMAGE, IMAGE_VALID_NUM, LABEL, XYXY
  from .utils import freeze_model_layers, lookup_mmdet_config, update_mmdet_config
label_desc: Use image node_id 16 for calling the MMDetAutoModelForObjectDetection obj's underlying member method code with example usage: obj.forward(batch, mode) and returns: rets, unknown