class_docstr: Load multi-channel images from a list of separate channel files.
  
  Required keys are "img_prefix" and "img_info" (a dict that must contain the
  key "filename", which is expected to be a list of filenames).
  Added or updated keys are "filename", "img", "img_shape",
  "ori_shape" (same as `img_shape`), "pad_shape" (same as `img_shape`),
  "scale_factor" (1.0) and "img_norm_cfg" (means=0 and stds=1).
  
  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 'color'.
      file_client_args (dict): Arguments to instantiate a FileClient.
          See :class:`mmcv.fileio.FileClient` for details.
          Defaults to ``dict(backend='disk')``.
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docstr: Call 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.
docstr_tok: ["Call","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","."]
code: def __call__(self, results):
      """Call 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.
      """
  
      if self.file_client is None:
          self.file_client = mmcv.FileClient(**self.file_client_args)
  
      if results["img_prefix"] is not None:
          filename = [
              osp.join(results["img_prefix"], fname)
              for fname in results["img_info"]["filename"]
          ]
      else:
          filename = results["img_info"]["filename"]
  
      img = []
      for name in filename:
          img_bytes = self.file_client.get(name)
          img.append(mmcv.imfrombytes(img_bytes, flag=self.color_type))
      img = np.stack(img, axis=-1)
      if self.to_float32:
          img = img.astype(np.float32)
  
      results["filename"] = filename
      results["ori_filename"] = results["img_info"]["filename"]
      results["img"] = img
      results["img_shape"] = img.shape
      results["ori_shape"] = img.shape
      # Set initial values for default meta_keys
      results["pad_shape"] = img.shape
      results["scale_factor"] = 1.0
      num_channels = 1 if len(img.shape) < 3 else img.shape[2]
      results["img_norm_cfg"] = dict(
          mean=np.zeros(num_channels, dtype=np.float32),
          std=np.ones(num_channels, dtype=np.float32),
          to_rgb=False,
      )
      return results
  
imports: import os.path
  import mmcv
  import numpy
  import pycocotools.mask
  from mmdet.core import BitmapMasks, PolygonMasks
  from ..builder import PIPELINES
label_desc: Use image node_id 2 for calling the LoadMultiChannelImageFromFiles obj's underlying member method code with example usage: obj.__call__(results) and returns: results