docstr: Predict results from a batch of inputs and data samples with post-
  processing.
  
  Args:
      batch_inputs (Tensor): Inputs, has shape (bs, dim, H, W).
      batch_data_samples (List[:obj:`DetDataSample`]): The batch
          data samples. It usually includes information such
          as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`.
      rescale (bool): Whether to rescale the results.
          Defaults to True.
  
  Returns:
      list[:obj:`DetDataSample`]: Detection results of the input images.
      Each DetDataSample usually contain 'pred_instances'. And the
      `pred_instances` usually contains following keys.
  
      - scores (Tensor): Classification scores, has a shape
        (num_instance, )
      - labels (Tensor): Labels of bboxes, has a shape
        (num_instances, ).
      - bboxes (Tensor): Has a shape (num_instances, 4),
        the last dimension 4 arrange as (x1, y1, x2, y2).
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code: def predict(
      self,
      batch_inputs: Tensor,
      batch_data_samples: SampleList,
      rescale: bool = True,
  ) -> SampleList:
      """Predict results from a batch of inputs and data samples with post-
      processing.
  
      Args:
          batch_inputs (Tensor): Inputs, has shape (bs, dim, H, W).
          batch_data_samples (List[:obj:`DetDataSample`]): The batch
              data samples. It usually includes information such
              as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`.
          rescale (bool): Whether to rescale the results.
              Defaults to True.
  
      Returns:
          list[:obj:`DetDataSample`]: Detection results of the input images.
          Each DetDataSample usually contain 'pred_instances'. And the
          `pred_instances` usually contains following keys.
  
          - scores (Tensor): Classification scores, has a shape
            (num_instance, )
          - labels (Tensor): Labels of bboxes, has a shape
            (num_instances, ).
          - bboxes (Tensor): Has a shape (num_instances, 4),
            the last dimension 4 arrange as (x1, y1, x2, y2).
      """
      assert self.eval_module in ["detr", "one-stage", "two-stage"]
  
      if self.use_lsj:
          for data_samples in batch_data_samples:
              img_metas = data_samples.metainfo
              input_img_h, input_img_w = img_metas["batch_input_shape"]
              img_metas["img_shape"] = [input_img_h, input_img_w]
  
      img_feats = self.extract_feat(batch_inputs)
      if self.with_bbox and self.eval_module == "one-stage":
          results_list = self.predict_bbox_head(
              img_feats, batch_data_samples, rescale=rescale
          )
      elif self.with_roi_head and self.eval_module == "two-stage":
          results_list = self.predict_roi_head(
              img_feats, batch_data_samples, rescale=rescale
          )
      else:
          results_list = self.predict_query_head(
              img_feats, batch_data_samples, rescale=rescale
          )
  
      batch_data_samples = self.add_pred_to_datasample(
          batch_data_samples, results_list
      )
      return batch_data_samples
  
imports: import copy
  from typing import Tuple, Union
  import torch
  import torch.nn
  from torch import Tensor
  from mmdet.models.detectors.base import BaseDetector
  from mmdet.registry import MODELS
  from mmdet.structures import OptSampleList, SampleList
  from mmdet.utils import InstanceList, OptConfigType, OptMultiConfig
label_desc: Use image node_id 10 for calling the CoDETR obj's underlying member method code with example usage: obj.predict(batch_inputs, batch_data_samples, rescale) and returns: batch_data_samples