class_docstr: Pack the inputs data for the ReID. The ``meta_info`` item is always
  populated. The contents of the ``meta_info`` dictionary depends on
  ``meta_keys``. By default this includes:
  
      - ``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``: scale of the image as a tuple (W, H).
      - ``scale_factor``: a float indicating the pre-processing scale.
      -  ``flip``: a boolean indicating if image flip transform was used.
      - ``flip_direction``: the flipping direction.
  Args:
      meta_keys (Sequence[str], optional): The meta keys to saved in the
          ``metainfo`` of the packed ``data_sample``.
code: def __init__(self, meta_keys: Sequence[str] = ()) -> None:
      self.meta_keys = self.default_meta_keys
      if meta_keys is not None:
          if isinstance(meta_keys, str):
              meta_keys = (meta_keys,)
          else:
              assert isinstance(
                  meta_keys, tuple
              ), "meta_keys must be str or tuple."
          self.meta_keys += meta_keys
  
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 1 to create a new PackReIDInputs object from inherited base classes: BaseTransform with example: obj = PackReIDInputs(meta_keys)