code: def adapt_to_chs(x, n):
      if not isinstance(x, (tuple, list)):
          x = tuple(repeat(x, n))
      elif len(x) != n:
          x_mean = np.mean(x).item()
          x = (x_mean,) * n
          _logger.warning(
              f"Pretrained mean/std different shape than model, using avg value {x}."
          )
      else:
          assert (
              len(x) == n
          ), "normalization stats must match image channels"
      return x
  
imports: import logging
  import random
  from contextlib import suppress
  from functools import partial
  from itertools import repeat
  from typing import Callable, Optional, Tuple, Union
  import torch
  import torch.utils.data
  import numpy
  from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from .dataset import IterableImageDataset, ImageDataset
  from .distributed_sampler import OrderedDistributedSampler, RepeatAugSampler
  from .random_erasing import RandomErasing
  from .mixup import FastCollateMixup
  from .transforms_factory import create_transform
label_desc: Use image node_id 2 for calling a global function with example usage: adapt_to_chs(x, n) and returns: x