class_docstr: Image processor for VAE.
  
  Args:
      do_resize (`bool`, *optional*, defaults to `True`):
          Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`. Can accept
          `height` and `width` arguments from [`image_processor.VaeImageProcessor.preprocess`] method.
      vae_scale_factor (`int`, *optional*, defaults to `8`):
          VAE scale factor. If `do_resize` is `True`, the image is automatically resized to multiples of this factor.
      resample (`str`, *optional*, defaults to `lanczos`):
          Resampling filter to use when resizing the image.
      do_normalize (`bool`, *optional*, defaults to `True`):
          Whether to normalize the image to [-1,1].
      do_binarize (`bool`, *optional*, defaults to `False`):
          Whether to binarize the image to 0/1.
      do_convert_rgb (`bool`, *optional*, defaults to be `False`):
          Whether to convert the images to RGB format.
      do_convert_grayscale (`bool`, *optional*, defaults to be `False`):
          Whether to convert the images to grayscale format.
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docstr: This function return the height and width that are downscaled to the next integer multiple of
  `vae_scale_factor`.
  
  Args:
      image(`PIL.Image.Image`, `np.ndarray` or `torch.Tensor`):
          The image input, can be a PIL image, numpy array or pytorch tensor. if it is a numpy array, should have
          shape `[batch, height, width]` or `[batch, height, width, channel]` if it is a pytorch tensor, should
          have shape `[batch, channel, height, width]`.
      height (`int`, *optional*, defaults to `None`):
          The height in preprocessed image. If `None`, will use the height of `image` input.
      width (`int`, *optional*`, defaults to `None`):
          The width in preprocessed. If `None`, will use the width of the `image` input.
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code: def get_default_height_width(
      self,
      image: [PIL.Image.Image, np.ndarray, torch.Tensor],
      height: Optional[int] = None,
      width: Optional[int] = None,
  ):
      """
      This function return the height and width that are downscaled to the next integer multiple of
      `vae_scale_factor`.
  
      Args:
          image(`PIL.Image.Image`, `np.ndarray` or `torch.Tensor`):
              The image input, can be a PIL image, numpy array or pytorch tensor. if it is a numpy array, should have
              shape `[batch, height, width]` or `[batch, height, width, channel]` if it is a pytorch tensor, should
              have shape `[batch, channel, height, width]`.
          height (`int`, *optional*, defaults to `None`):
              The height in preprocessed image. If `None`, will use the height of `image` input.
          width (`int`, *optional*`, defaults to `None`):
              The width in preprocessed. If `None`, will use the width of the `image` input.
      """
  
      if height is None:
          if isinstance(image, PIL.Image.Image):
              height = image.height
          elif isinstance(image, torch.Tensor):
              height = image.shape[2]
          else:
              height = image.shape[1]
  
      if width is None:
          if isinstance(image, PIL.Image.Image):
              width = image.width
          elif isinstance(image, torch.Tensor):
              width = image.shape[3]
          else:
              width = image.shape[2]
  
      width, height = (
          x - x % self.config.vae_scale_factor for x in (width, height)
      )  # resize to integer multiple of vae_scale_factor
  
      return height, width
  
imports: import warnings
  from typing import List, Optional, Union
  import numpy
  import PIL.Image
  import torch
  from PIL import Image
  from .configuration_utils import ConfigMixin, register_to_config
  from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate
label_desc: Use image node_id 10 for calling the VaeImageProcessor obj's underlying member method code with example usage: obj.get_default_height_width(image, height, width) and returns: height,  width