class_docstr: Mixin that contain utilities for preparing image features.
class_docstr_tok: ["Mixin","that","contain","utilities","for","preparing","image","features","."]
docstr: Flips the channel order of `image` from RGB to BGR, or vice versa. Note that this will trigger a conversion of
  `image` to a NumPy array if it's a PIL Image.
  
  Args:
      image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
          The image whose color channels to flip. If `np.ndarray` or `torch.Tensor`, the channel dimension should
          be first.
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code: def flip_channel_order(self, image):
      """
      Flips the channel order of `image` from RGB to BGR, or vice versa. Note that this will trigger a conversion of
      `image` to a NumPy array if it's a PIL Image.
  
      Args:
          image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
              The image whose color channels to flip. If `np.ndarray` or `torch.Tensor`, the channel dimension should
              be first.
      """
      self._ensure_format_supported(image)
  
      if isinstance(image, PIL.Image.Image):
          image = self.to_numpy_array(image)
  
      return image[::-1, :, :]
  
imports: import os
  from typing import TYPE_CHECKING, List, Tuple, Union
  import numpy
  from packaging import version
  import requests
  from .utils import ExplicitEnum, is_jax_tensor, is_tf_tensor, is_torch_available, is_torch_tensor, is_vision_available, to_numpy
  from .utils.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
label_desc: Use image node_id 10 for calling the ImageFeatureExtractionMixin obj's underlying member method code with example usage: obj.flip_channel_order(image) and returns: image