class_docstr: Constructs a VideoMAE feature extractor. This feature extractor can be used to prepare videos for the model.
  
  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 shorter edge of the input to a certain `size`.
      size (`int`, *optional*, defaults to 224):
          Resize the shorter edge of the input to the given 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_center_crop (`bool`, *optional*, defaults to `True`):
          Whether to center crop the input to a certain `size`.
      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.
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code: def resize_video(self, video, size, resample="bilinear"):
      return [
          self.resize(frame, size, resample, default_to_square=False)
          for frame in video
      ]
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/videomae/feature_extraction_videomae.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 ImageFeatureExtractionMixin, ImageInput, is_torch_tensor
  from ...utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, TensorType, logging
label_desc: Use image node_id 2 for calling the VideoMAEFeatureExtractor obj's underlying member method code with example usage: obj.resize_video(video, size, resample) and returns: unknown