class_docstr: Create an operator that sharpens image with: out = 2 * image - gaussian_blur2d(image).
  
  Args:
      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 sharpened tensor with shape :math:`(B,C,H,W)`.
  
  Shape:
      - Input: :math:`(B, C, H, W)`
      - Output: :math:`(B, C, H, W)`
  
  .. note::
     See a working example `here <https://kornia.github.io/tutorials/nbs/unsharp_mask.html>`__.
  
  Examples:
      >>> input = torch.rand(2, 4, 5, 5)
      >>> sharpen = UnsharpMask((3, 3), (1.5, 1.5))
      >>> output = sharpen(input)
      >>> output.shape
      torch.Size([2, 4, 5, 5])
code: def forward(self, input: Tensor) -> Tensor:
      return unsharp_mask(
          input, self.kernel_size, self.sigma, self.border_type
      )
  
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 2 for calling the UnsharpMask obj's underlying member method code with example usage: obj.forward(input) and returns: unsharp_mask