class_docstr: Constructs a LayoutLMv3 feature extractor. This can be used to resize + normalize document images, as well as to
  apply OCR on them in order to get a list of words and normalized bounding boxes.
  
  This feature extractor inherits from [`~feature_extraction_utils.PreTrainedFeatureExtractor`] 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 to a certain `size`.
      size (`int` or `Tuple(int)`, *optional*, defaults to 224):
          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 (`List[int]`, defaults to `[0.5, 0.5, 0.5]`):
          The sequence of means for each channel, to be used when normalizing images.
      image_std (`List[int]`, defaults to `[0.5, 0.5, 0.5]`):
          The sequence of standard deviations for each channel, to be used when normalizing images.
      apply_ocr (`bool`, *optional*, defaults to `True`):
          Whether to apply the Tesseract OCR engine to get words + normalized bounding boxes.
      ocr_lang (`str`, *optional*):
          The language, specified by its ISO code, to be used by the Tesseract OCR engine. By default, English is
          used.
      tesseract_config (`str`, *optional*):
          Any additional custom configuration flags that are forwarded to the `config` parameter when calling
          Tesseract. For example: '--psm 6'.
  
          <Tip>
  
          LayoutLMv3FeatureExtractor uses Google's Tesseract OCR engine under the hood.
  
          </Tip>
code: def __init__(
      self,
      do_resize=True,
      size=224,
      resample=PILImageResampling.BILINEAR,
      do_normalize=True,
      image_mean=None,
      image_std=None,
      apply_ocr=True,
      ocr_lang=None,
      tesseract_config="",
      **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_STANDARD_MEAN
      )
      self.image_std = (
          image_std if image_std is not None else IMAGENET_STANDARD_STD
      )
      self.apply_ocr = apply_ocr
      self.ocr_lang = ocr_lang
      self.tesseract_config = tesseract_config
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/layoutlmv3/feature_extraction_layoutlmv3.py
imports: from typing import List, 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_STANDARD_MEAN, IMAGENET_STANDARD_STD, ImageFeatureExtractionMixin, is_torch_tensor
  from ...utils import TensorType, is_pytesseract_available, logging, requires_backends
label_desc: Use image node_id 1 to create a new LayoutLMv3FeatureExtractor object from inherited base classes: FeatureExtractionMixin, ImageFeatureExtractionMixin with example: obj = LayoutLMv3FeatureExtractor(do_resize, size, resample, do_normalize, image_mean, image_std, apply_ocr, ocr_lang, tesseract_config)