class_docstr: This is designed specifically for the GLIP algorithm.
  
  In order to completely match the official performance, we need to perform
  special calculations in the encoding and decoding processes, such as
  additional +1 and -1 calculations. However, this is not a user-friendly
  design.
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docstr: Apply transformation `pred_bboxes` to `boxes`.
  
  Args:
      bboxes (torch.Tensor or :obj:`BaseBoxes`): Basic boxes. Shape
          (B, N, 4) or (N, 4)
      pred_bboxes (Tensor): Encoded offsets with respect to each roi.
         Has shape (B, N, num_classes * 4) or (B, N, 4) or
         (N, num_classes * 4) or (N, 4). Note N = num_anchors * W * H
         when rois is a grid of anchors.Offset encoding follows [1]_.
      max_shape (Sequence[int] or torch.Tensor or Sequence[
         Sequence[int]],optional): Maximum bounds for boxes, specifies
         (H, W, C) or (H, W). If bboxes shape is (B, N, 4), then
         the max_shape should be a Sequence[Sequence[int]]
         and the length of max_shape should also be B.
      wh_ratio_clip (float, optional): The allowed ratio between
          width and height.
  
  Returns:
      Union[torch.Tensor, :obj:`BaseBoxes`]: Decoded boxes.
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code: def decode(
      self,
      bboxes: Union[Tensor, BaseBoxes],
      pred_bboxes: Tensor,
      max_shape: Optional[
          Union[Sequence[int], Tensor, Sequence[Sequence[int]]]
      ] = None,
      wh_ratio_clip: Optional[float] = 16 / 1000,
  ) -> Union[Tensor, BaseBoxes]:
      """Apply transformation `pred_bboxes` to `boxes`.
  
      Args:
          bboxes (torch.Tensor or :obj:`BaseBoxes`): Basic boxes. Shape
              (B, N, 4) or (N, 4)
          pred_bboxes (Tensor): Encoded offsets with respect to each roi.
             Has shape (B, N, num_classes * 4) or (B, N, 4) or
             (N, num_classes * 4) or (N, 4). Note N = num_anchors * W * H
             when rois is a grid of anchors.Offset encoding follows [1]_.
          max_shape (Sequence[int] or torch.Tensor or Sequence[
             Sequence[int]],optional): Maximum bounds for boxes, specifies
             (H, W, C) or (H, W). If bboxes shape is (B, N, 4), then
             the max_shape should be a Sequence[Sequence[int]]
             and the length of max_shape should also be B.
          wh_ratio_clip (float, optional): The allowed ratio between
              width and height.
  
      Returns:
          Union[torch.Tensor, :obj:`BaseBoxes`]: Decoded boxes.
      """
      bboxes = get_box_tensor(bboxes)
      assert pred_bboxes.size(0) == bboxes.size(0)
      if pred_bboxes.ndim == 3:
          assert pred_bboxes.size(1) == bboxes.size(1)
  
      if pred_bboxes.ndim == 2 and not torch.onnx.is_in_onnx_export():
          # single image decode
          decoded_bboxes = delta2bbox_glip(
              bboxes,
              pred_bboxes,
              self.means,
              self.stds,
              max_shape,
              wh_ratio_clip,
              self.clip_border,
              self.add_ctr_clamp,
              self.ctr_clamp,
          )
      else:
          raise NotImplementedError()
  
      if self.use_box_type:
          assert decoded_bboxes.size(-1) == 4, (
              "Cannot warp decoded boxes with box type when decoded boxes"
              "have shape of (N, num_classes * 4)"
          )
          decoded_bboxes = HorizontalBoxes(decoded_bboxes)
      return decoded_bboxes
  
imports: import warnings
  from typing import Optional, Sequence, Union
  import numpy
  import torch
  from torch import Tensor
  from mmdet.registry import TASK_UTILS
  from mmdet.structures.bbox import BaseBoxes, HorizontalBoxes, get_box_tensor
  from .base_bbox_coder import BaseBBoxCoder
label_desc: Use image node_id 2 for calling the DeltaXYWHBBoxCoderForGLIP obj's underlying member method code with example usage: obj.decode(bboxes, pred_bboxes, max_shape, wh_ratio_clip) and returns: decoded_bboxes