code: def remove_channel(
      self, channel, data_group_labels=None, channel_dim=-1
  ):
      # TODO: Add functionality for removing multiple channels
  
      if data_group_labels is None:
          data_groups = list(self.data_groups.values())
      else:
          data_groups = [
              self.data_groups[label] for label in data_group_labels
          ]
  
      for data_group in data_groups:
          data_group.base_case = np.delete(
              data_group.base_case, channel, axis=channel_dim
          )
  
          # Perhaps should not use tuples for output shape.
          if data_group.output_shape is not None:
              output_shape = list(data_group.output_shape)
              output_shape[channel_dim] -= 1
              data_group.output_shape = tuple(output_shape)
  
      return
  
imports: import os
  import csv
  import numpy
  import tables
  import copy
  from tqdm import tqdm
  from deepneuro.augmentation.augment import Copy
  from deepneuro.utilities.conversion import read_image_files
  from deepneuro.data.data_group import DataGroup
  from deepneuro.data.data_load import parse_directories, parse_filepaths, parse_hdf5, parse_csv, parse_numpy
  from deepneuro.utilities.util import add_parameter
label_desc: Use image node_id 22 for calling the DataCollection obj's underlying member method code with example usage: obj.remove_channel(channel, data_group_labels, channel_dim) without return types