code: def check_and_correct(self, cfg):
      model_cfg = cfg.model_cfg
      train_dataset_cfg = cfg.train_dataset_cfg
      val_dataset_cfg = cfg.val_dataset_cfg
  
      assert (
          train_dataset_cfg != {}
      ), "No train_dataset specified in the configuration file."
      if train_dataset_cfg["type"] != "Dataset":
          return
      if val_dataset_cfg != {}:
          assert (
              val_dataset_cfg["type"] == "Dataset"
          ), "The type of train_dataset and val_dataset must be the same"
  
      # If the model has backbone, in_channels is the input params of backbone.
      # Otherwise, in_channels is the input params of the model.
      value_set = set()
      x = utils.get_in_channels(model_cfg)
      if x is not None:
          value_set.add(x)
      if "img_channels" in train_dataset_cfg:
          value_set.add(train_dataset_cfg["img_channels"])
      if "img_channels" in val_dataset_cfg:
          value_set.add(val_dataset_cfg["img_channels"])
  
      if len(value_set) > 1:
          raise ValueError(
              "`in_channels` is not consistent: {}. Please set it "
              "consistently in model or train_dataset or val_dataset".format(
                  value_set
              )
          )
      if (
          len(value_set) == 1
          and utils.get_in_channels(model_cfg) is None
      ):
          logger.warning(
              "Add the `in_channels` in train_dataset and val_dataset "
              "config to model config. We suggest you manually set `in_channels` in model config."
          )
          model_cfg = utils.set_in_channels(model_cfg, value_set.pop())
          cfg.dic["model"] = model_cfg
  
imports: import copy
  from paddleseg.utils import logger
  from paddleseg.utils import utils
label_desc: Use image node_id 1 for calling the DefaultSyncImgChannelsRule obj's underlying member method code with example usage: obj.check_and_correct(cfg) without return types