class_docstr: Constructs a SegFormer 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 the input based on a certain `size`.
      size (`int` or `Tuple(int)`, *optional*, defaults to 512):
          Resize the input to the given size. If a tuple is provided, it should be (width, height). If only an
          integer is provided, then the input will be resized to (size, size). Only has an effect if `do_resize` is
          set to `True`.
      resample (`int`, *optional*, defaults to `PIL.Image.Resampling.BILINEAR`):
          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`.
      do_normalize (`bool`, *optional*, defaults to `True`):
          Whether or not to normalize the input with mean and standard deviation.
      image_mean (`int`, *optional*, defaults to `[0.485, 0.456, 0.406]`):
          The sequence of means for each channel, to be used when normalizing images. Defaults to the ImageNet mean.
      image_std (`int`, *optional*, defaults to `[0.229, 0.224, 0.225]`):
          The sequence of standard deviations for each channel, to be used when normalizing images. Defaults to the
          ImageNet std.
      reduce_labels (`bool`, *optional*, defaults to `False`):
          Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is
          used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The
          background label will be replaced by 255.
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code: def __init__(
      self,
      do_resize=True,
      size=512,
      resample=PILImageResampling.BILINEAR,
      do_normalize=True,
      image_mean=None,
      image_std=None,
      reduce_labels=False,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.do_resize = do_resize
      self.size = size
      self.resample = resample
      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
      )
      self.reduce_labels = reduce_labels
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/segformer/feature_extraction_segformer.py
imports: from typing import List, Optional, Tuple, 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, is_torch_available, logging
label_desc: Use image node_id 1 to create a new SegformerFeatureExtractor object from inherited base classes: FeatureExtractionMixin, ImageFeatureExtractionMixin with example: obj = SegformerFeatureExtractor(do_resize, size, resample, do_normalize, image_mean, image_std, reduce_labels)