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: Add the batch color counts to the color_averages dict, and update counter.
docstr_tok: ["Add","the","batch","color","counts","to","the","color_averages","dict",",","and","update","counter","."]
code: def update(
      self,
      context: Context,
      batch: BatchWrapper,
      dataset_kind: DatasetKind,
  ):
      """Add the batch color counts to the color_averages dict, and update counter."""
  
      self._color_averages[dataset_kind.value] += sum_pixel_values(
          batch
      )  # add to the color_averages dict
      # count the number of pixels we have summed
      self._pixel_count[dataset_kind.value] += count_pixels_in_batch(
          batch
      )
  
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 3 for calling the ColorAveragesCheck obj's underlying member method code with example usage: obj.update(context, batch, dataset_kind) without return types