code: def unsharp_mask(
      input: Tensor,
      kernel_size: tuple[int, int] | int,
      sigma: tuple[float, float] | Tensor,
      border_type: str = "reflect",
  ) -> Tensor:
      r"""Create an operator that sharpens a tensor by applying operation out = 2 * image - gaussian_blur2d(image).
  
      .. image:: _static/img/unsharp_mask.png
  
      Args:
          input: the input tensor with shape :math:`(B,C,H,W)`.
          kernel_size: the size of the kernel.
          sigma: the standard deviation of the kernel.
          border_type: the padding mode to be applied before convolving.
            The expected modes are: ``'constant'``, ``'reflect'``,
            ``'replicate'`` or ``'circular'``.
  
      Returns:
          the blurred tensor with shape :math:`(B,C,H,W)`.
  
      Examples:
          >>> input = torch.rand(2, 4, 5, 5)
          >>> output = unsharp_mask(input, (3, 3), (1.5, 1.5))
          >>> output.shape
          torch.Size([2, 4, 5, 5])
      """
      data_blur: Tensor = gaussian_blur2d(
          input, kernel_size, sigma, border_type
      )
      data_sharpened: Tensor = input + (input - data_blur)
      return data_sharpened
  
file_path: kornia/kornia/filters/unsharp.py
imports: from __future__ import annotations
  from kornia.core import Module, Tensor
  from .gaussian import gaussian_blur2d
label_desc: Use image node_id 1 for calling a global function with example usage: unsharp_mask(input, kernel_size, sigma, border_type) and returns: data_sharpened