class_docstr: Create an operator that blurs a tensor using a Gaussian filter.
  
  The operator smooths the given tensor with a gaussian kernel by convolving
  it to each channel. It supports batched operation.
  
  Arguments:
      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'``. Default: ``'reflect'``.
      separable: run as composition of two 1d-convolutions.
  
  Returns:
      the blurred tensor.
  
  Shape:
      - Input: :math:`(B, C, H, W)`
      - Output: :math:`(B, C, H, W)`
  
  Examples::
  
      >>> input = torch.rand(2, 4, 5, 5)
      >>> gauss = GaussianBlur2d((3, 3), (1.5, 1.5))
      >>> output = gauss(input)  # 2x4x5x5
      >>> output.shape
      torch.Size([2, 4, 5, 5])
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code: def forward(self, input: Tensor) -> Tensor:
      return gaussian_blur2d(
          input,
          self.kernel_size,
          self.sigma,
          self.border_type,
          self.separable,
      )
  
file_path: kornia/kornia/filters/gaussian.py
imports: from __future__ import annotations
  from typing import Any
  from kornia.core import Module, Tensor, tensor
  from kornia.core.check import KORNIA_CHECK_IS_TENSOR
  from kornia.utils import deprecated
  from .filter import filter2d, filter2d_separable
  from .kernels import _unpack_2d_ks, get_gaussian_kernel1d, get_gaussian_kernel2d
label_desc: Use image node_id 3 for calling the GaussianBlur2d obj's underlying member method code with example usage: obj.forward(input) and returns: gaussian_blur2d