class_docstr: Check if the average of each color channel is the same between the train and test dataset.
class_docstr_tok: ["Check","if","the","average","of","each","color","channel","is","the","same","between","the","train","and","test","dataset","."]
docstr: Init the check and enable customization of the channel_names.
docstr_tok: ["Init","the","check","and","enable","customization","of","the","channel_names","."]
code: def __init__(self, channel_names: t.Tuple[str] = None, **kwargs):
      """Init the check and enable customization of the channel_names."""
      super().__init__(**kwargs)
      if channel_names is None:
          self.channel_names = ("R", "G", "B")
  
imports: import typing
  import numpy
  from deepchecks.core.check_result import CheckResult
  from deepchecks.core.checks import DatasetKind
  from deepchecks.core.condition import ConditionCategory
  from deepchecks.vision.base_checks import TrainTestCheck
  from deepchecks.vision.context import Context
  from deepchecks.vision.vision_data.batch_wrapper import BatchWrapper
  from deepchecks.vision.datasets.detection.coco_torch import load_dataset
  import pandas
  import plotly.express
  from deepchecks.core import ConditionResult
label_desc: Use image node_id 1 to create a new ColorAveragesCheck object from inherited base classes: TrainTestCheck with example: obj = ColorAveragesCheck(channel_names)