class_docstr: Pack the inputs data for the detection / semantic segmentation /
  panoptic segmentation.
  
  The ``img_meta`` item is always populated.  The contents of the
  ``img_meta`` dictionary depends on ``meta_keys``. By default this includes:
  
      - ``img_id``: id of the image
  
      - ``img_path``: path to the image file
  
      - ``ori_shape``: original shape of the image as a tuple (h, w)
  
      - ``img_shape``: shape of the image input to the network as a tuple             (h, w).  Note that images may be zero padded on the             bottom/right if the batch tensor is larger than this shape.
  
      - ``scale_factor``: a float indicating the preprocessing scale
  
      - ``flip``: a boolean indicating if image flip transform was used
  
      - ``flip_direction``: the flipping direction
  
  Args:
      meta_keys (Sequence[str], optional): Meta keys to be converted to
          ``mmcv.DataContainer`` and collected in ``data[img_metas]``.
          Default: ``('img_id', 'img_path', 'ori_shape', 'img_shape',
          'scale_factor', 'flip', 'flip_direction')``
code: def __repr__(self) -> str:
      repr_str = self.__class__.__name__
      repr_str += f"(meta_keys={self.meta_keys})"
      return repr_str
  
imports: from typing import Optional, Sequence
  import numpy
  from mmcv.transforms import to_tensor
  from mmcv.transforms.base import BaseTransform
  from mmengine.structures import InstanceData, PixelData
  from mmdet.registry import TRANSFORMS
  from mmdet.structures import DetDataSample, ReIDDataSample, TrackDataSample
  from mmdet.structures.bbox import BaseBoxes
label_desc: Use image node_id 3 for calling the PackDetInputs obj's underlying member method code with example usage: obj.__repr__() and returns: repr_str