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``.
docstr: Method to pack the input data.
  Args:
      results (dict): Result dict from the data pipeline.
  Returns:
      dict:
      - 'inputs' (dict[Tensor]): The forward data of models.
      - 'data_samples' (obj:`ReIDDataSample`): The meta info of the
          sample.
code: def transform(self, results: dict) -> dict:
      """Method to pack the input data.
      Args:
          results (dict): Result dict from the data pipeline.
      Returns:
          dict:
          - 'inputs' (dict[Tensor]): The forward data of models.
          - 'data_samples' (obj:`ReIDDataSample`): The meta info of the
              sample.
      """
      packed_results = dict(inputs=dict(), data_samples=None)
      assert "img" in results, "Missing the key ``img``."
      _type = type(results["img"])
      label = results["gt_label"]
  
      if _type == list:
          img = results["img"]
          label = np.stack(label, axis=0)  # (N,)
          assert all(
              [type(v) == _type for v in results.values()]
          ), "All items in the results must have the same type."
      else:
          img = [results["img"]]
  
      img = np.stack(img, axis=3)  # (H, W, C, N)
      img = img.transpose(3, 2, 0, 1)  # (N, C, H, W)
      img = np.ascontiguousarray(img)
  
      packed_results["inputs"] = to_tensor(img)
  
      data_sample = ReIDDataSample()
      data_sample.set_gt_label(label)
  
      meta_info = dict()
      for key in self.meta_keys:
          meta_info[key] = results[key]
      data_sample.set_metainfo(meta_info)
      packed_results["data_samples"] = data_sample
  
      return packed_results
  
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 2 for calling the PackReIDInputs obj's underlying member method code with example usage: obj.transform(results) and returns: packed_results