class_docstr: Resize the input torch.Tensor to the given size.
  
  Args:
      size: Desired output size. If size is a sequence like (h, w),
          output size will be matched to this. If size is an int, smaller edge of the image will
          be matched to this number. i.e, if height > width, then image will be rescaled
          to (size * height / width, size)
      interpolation:  algorithm used for upsampling: ``'nearest'`` | ``'linear'`` | ``'bilinear'`` |
          'bicubic' | 'trilinear' | 'area'.
      align_corners: interpolation flag.
      side: Corresponding side if ``size`` is an integer. Can be one of ``'short'``, ``'long'``, ``'vert'``,
          or ``'horz'``.
      antialias: if True, then image will be filtered with Gaussian before downscaling.
          No effect for upscaling.
  
  Returns:
      The resized tensor with the shape of the given size.
  
  Example:
      >>> img = torch.rand(1, 3, 4, 4)
      >>> out = Resize((6, 8))(img)
      >>> print(out.shape)
      torch.Size([1, 3, 6, 8])
  
  .. raw:: html
  
      <gradio-app space="kornia/kornia-resize-antialias"></gradio-app>
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code: def __init__(
      self,
      size: Union[int, Tuple[int, int]],
      interpolation: str = "bilinear",
      align_corners: Optional[bool] = None,
      side: str = "short",
      antialias: bool = False,
  ) -> None:
      super().__init__()
      self.size: Union[int, Tuple[int, int]] = size
      self.interpolation: str = interpolation
      self.align_corners: Optional[bool] = align_corners
      self.side: str = side
      self.antialias: bool = antialias
  
imports: from typing import Optional, Tuple, Union
  import torch
  from torch import nn
  from kornia.core import ones, ones_like, zeros
  from kornia.filters import gaussian_blur2d
  from kornia.utils import _extract_device_dtype
  from kornia.utils.image import perform_keep_shape_image
  from kornia.utils.misc import eye_like
  from .imgwarp import get_affine_matrix2d, get_projective_transform, get_rotation_matrix2d, warp_affine, warp_affine3d
label_desc: Use image node_id 1 to create a new Resize object from inherited base classes: nn with example: obj = Resize(size, interpolation, align_corners, side, antialias)