class_docstr: Constructs a ConvNeXT feature extractor.
  
  This feature extractor inherits from [`FeatureExtractionMixin`] which contains most of the main methods. Users
  should refer to this superclass for more information regarding those methods.
  
  Args:
      do_resize (`bool`, *optional*, defaults to `True`):
          Whether to resize (and optionally center crop) the input to a certain `size`.
      size (`int`, *optional*, defaults to 224):
          Resize the input to the given size. If 384 or larger, the image is resized to (`size`, `size`). Else, the
          smaller edge of the image will be matched to int(`size`/ `crop_pct`), after which the image is cropped to
          `size`. Only has an effect if `do_resize` is set to `True`.
      resample (`int`, *optional*, defaults to `PIL.Image.Resampling.BICUBIC`):
          An optional resampling filter. This can be one of `PIL.Image.Resampling.NEAREST`,
          `PIL.Image.Resampling.BOX`, `PIL.Image.Resampling.BILINEAR`, `PIL.Image.Resampling.HAMMING`,
          `PIL.Image.Resampling.BICUBIC` or `PIL.Image.Resampling.LANCZOS`. Only has an effect if `do_resize` is set
          to `True`.
      crop_pct (`float`, *optional*):
          The percentage of the image to crop. If `None`, then a cropping percentage of 224 / 256 is used. Only has
          an effect if `do_resize` is set to `True` and `size` < 384.
      do_normalize (`bool`, *optional*, defaults to `True`):
          Whether or not to normalize the input with mean and standard deviation.
      image_mean (`List[int]`, defaults to `[0.485, 0.456, 0.406]`):
          The sequence of means for each channel, to be used when normalizing images.
      image_std (`List[int]`, defaults to `[0.229, 0.224, 0.225]`):
          The sequence of standard deviations for each channel, to be used when normalizing images.
code: def __init__(
      self,
      do_resize=True,
      size=224,
      resample=PILImageResampling.BICUBIC,
      crop_pct=None,
      do_normalize=True,
      image_mean=None,
      image_std=None,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.do_resize = do_resize
      self.size = size
      self.resample = resample
      self.crop_pct = crop_pct
      self.do_normalize = do_normalize
      self.image_mean = (
          image_mean
          if image_mean is not None
          else IMAGENET_DEFAULT_MEAN
      )
      self.image_std = (
          image_std if image_std is not None else IMAGENET_DEFAULT_STD
      )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/convnext/feature_extraction_convnext.py
imports: from typing import Optional, Union
  import numpy
  from PIL import Image
  from transformers.image_utils import PILImageResampling
  from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
  from ...image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, ImageFeatureExtractionMixin, ImageInput, is_torch_tensor
  from ...utils import TensorType, logging
label_desc: Use image node_id 1 to create a new ConvNextFeatureExtractor object from inherited base classes: FeatureExtractionMixin, ImageFeatureExtractionMixin with example: obj = ConvNextFeatureExtractor(do_resize, size, resample, crop_pct, do_normalize, image_mean, image_std)