class_docstr: Resize to size.
  
  Args:
      size: Size (h, w) in pixels of the resized region or just one side.
      side: Which side to resize, if size is only of type int.
      resample: Resampling mode.
      align_corners: interpolation flag.
      antialias: if True, then image will be filtered with Gaussian before downscaling. No effect for upscaling.
      keepdim: whether to keep the output shape the same as input (True) or broadcast it
          to the batch form (False).
class_docstr_tok: ["Resize","to","size",".","Args",":","size",":","Size","(","h",",","w",")","in","pixels","of","the","resized","region","or","just","one","side",".","side",":","Which","side","to","resize",",","if","size","is","only","of","type","int",".","resample",":","Resampling","mode",".","align_corners",":","interpolation","flag",".","antialias",":","if","True",",","then","image","will","be","filtered","with","Gaussian","before","downscaling",".","No","effect","for","upscaling",".","keepdim",":","whether","to","keep","the","output","shape","the","same","as","input","(","True",")","or","broadcast","it","to","the","batch","form","(","False",")","."]
code: def compute_transformation(
      self,
      input: Tensor,
      params: Dict[str, Tensor],
      flags: Dict[str, Any],
  ) -> Tensor:
      if params["output_size"] == input.shape[-2:]:
          return eye_like(3, input)
  
      transform: Tensor = torch.as_tensor(
          get_perspective_transform(params["src"], params["dst"]),
          dtype=input.dtype,
          device=input.device,
      )
      transform = transform.expand(input.shape[0], -1, -1)
      return transform
  
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 2 for calling the Resize obj's underlying member method code with example usage: obj.compute_transformation(input, params, flags) and returns: transform, eye_like