code: def scale(
      tensor: torch.Tensor,
      scale_factor: torch.Tensor,
      center: Union[None, torch.Tensor] = None,
      mode: str = "bilinear",
      padding_mode: str = "zeros",
      align_corners: bool = True,
  ) -> torch.Tensor:
      r"""Scale the tensor by a factor.
  
      .. image:: _static/img/scale.png
  
      Args:
          tensor: The image tensor to be warped in shapes of :math:`(B, C, H, W)`.
          scale_factor: The scale factor apply. The tensor
            must have a shape of (B) or (B, 2), where B is batch size.
            If (B), isotropic scaling will perform.
            If (B, 2), x-y-direction specific scaling will perform.
          center: The center through which to scale. The tensor
            must have a shape of (B, 2), where B is batch size and last
            dimension contains cx and cy.
          mode: interpolation mode to calculate output values
            ``'bilinear'`` | ``'nearest'``.
          padding_mode: padding mode for outside grid values
            ``'zeros'`` | ``'border'`` | ``'reflection'``.
          align_corners: interpolation flag.
  
      Returns:
          The scaled tensor with the same shape as the input.
  
      Example:
          >>> img = torch.rand(1, 3, 4, 4)
          >>> scale_factor = torch.tensor([[2., 2.]])
          >>> out = scale(img, scale_factor)
          >>> print(out.shape)
          torch.Size([1, 3, 4, 4])
      """
      if not isinstance(tensor, torch.Tensor):
          raise TypeError(
              f"Input tensor type is not a torch.Tensor. Got {type(tensor)}"
          )
  
      if not isinstance(scale_factor, torch.Tensor):
          raise TypeError(
              f"Input scale_factor type is not a torch.Tensor. Got {type(scale_factor)}"
          )
  
      if len(scale_factor.shape) == 1:
          # convert isotropic scaling to x-y direction
          scale_factor = scale_factor.repeat(1, 2)
  
      # compute the tensor center
      if center is None:
          center = _compute_tensor_center(tensor)
  
      # compute the rotation matrix
      # TODO: add broadcasting to get_rotation_matrix2d for center
      center = center.expand(tensor.shape[0], -1)
      scale_factor = scale_factor.expand(tensor.shape[0], 2)
      scaling_matrix: torch.Tensor = _compute_scaling_matrix(
          scale_factor, center
      )
  
      # warp using the affine transform
      return affine(
          tensor,
          scaling_matrix[..., :2, :3],
          mode,
          padding_mode,
          align_corners,
      )
  
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 13 for calling a global function with example usage: scale(tensor, scale_factor, center, mode, padding_mode, align_corners) and returns: affine