class_docstr: Rescale an image so that minimum side is equal to max_size, keeping the aspect ratio of the initial image.
  
  Args:
      max_size: maximum size of the image after the transformation.
class_docstr_tok: ["Rescale","an","image","so","that","minimum","side","is","equal","to","max_size",",","keeping","the","aspect","ratio","of","the","initial","image",".","Args",":","max_size",":","maximum","size","of","the","image","after","the","transformation","."]
code: def __init__(
      self,
      max_size: int,
      resample: Union[str, int, Resample] = Resample.BILINEAR.name,
      align_corners: bool = True,
      p: float = 1.0,
  ) -> None:
      # TODO: Support max_size list input to randomly select from
      super().__init__(
          size=max_size,
          side="short",
          resample=resample,
          align_corners=align_corners,
          p=p,
      )
  
file_path: kornia/kornia/augmentation/_2d/geometric/resize.py
imports: from typing import Any, Dict, Optional, Tuple, Union
  import torch
  from kornia.augmentation import random_generator
  from kornia.augmentation._2d.geometric.base import GeometricAugmentationBase2D
  from kornia.constants import Resample
  from kornia.core import Tensor
  from kornia.geometry.transform import crop_by_transform_mat, get_perspective_transform, resize
  from kornia.utils import eye_like
label_desc: Use image node_id 1 to create a new SmallestMaxSize object from inherited base classes: Resize with example: obj = SmallestMaxSize(max_size, resample, align_corners, p)