class_docstr: Load multi-channel images from a list of separate channel files.
  
  Required Keys:
  
  - img_path
  
  Modified Keys:
  
  - img
  - img_shape
  - ori_shape
  
  Args:
      to_float32 (bool): Whether to convert the loaded image to a float32
          numpy array. If set to False, the loaded image is an uint8 array.
          Defaults to False.
      color_type (str): The flag argument for :func:``mmcv.imfrombytes``.
          Defaults to 'unchanged'.
      imdecode_backend (str): The image decoding backend type. The backend
          argument for :func:``mmcv.imfrombytes``.
          See :func:``mmcv.imfrombytes`` for details.
          Defaults to 'cv2'.
      file_client_args (dict): Arguments to instantiate the
          corresponding backend in mmdet <= 3.0.0rc6. Defaults to None.
      backend_args (dict, optional): Arguments to instantiate the
          corresponding backend in mmdet >= 3.0.0rc7. Defaults to None.
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docstr: Transform functions to load multiple images and get images meta
  information.
  
  Args:
      results (dict): Result dict from :obj:`mmdet.CustomDataset`.
  
  Returns:
      dict: The dict contains loaded images and meta information.
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code: def transform(self, results: dict) -> dict:
      """Transform functions to load multiple images and get images meta
      information.
  
      Args:
          results (dict): Result dict from :obj:`mmdet.CustomDataset`.
  
      Returns:
          dict: The dict contains loaded images and meta information.
      """
  
      assert isinstance(results["img_path"], list)
      img = []
      for name in results["img_path"]:
          img_bytes = get(name, backend_args=self.backend_args)
          img.append(
              mmcv.imfrombytes(
                  img_bytes,
                  flag=self.color_type,
                  backend=self.imdecode_backend,
              )
          )
      img = np.stack(img, axis=-1)
      if self.to_float32:
          img = img.astype(np.float32)
  
      results["img"] = img
      results["img_shape"] = img.shape[:2]
      results["ori_shape"] = img.shape[:2]
      return results
  
imports: from typing import Optional, Tuple, Union
  import mmcv
  import numpy
  import pycocotools.mask
  import torch
  from mmcv.transforms import BaseTransform
  from mmcv.transforms import LoadAnnotations
  from mmcv.transforms import LoadImageFromFile
  from mmengine.fileio import get
  from mmengine.structures import BaseDataElement
  from mmdet.registry import TRANSFORMS
  from mmdet.structures.bbox import get_box_type
  from mmdet.structures.bbox.box_type import autocast_box_type
  from mmdet.structures.mask import BitmapMasks, PolygonMasks
label_desc: Use image node_id 2 for calling the LoadMultiChannelImageFromFiles obj's underlying member method code with example usage: obj.transform(results) and returns: results