code: def max_blur_pool2d(
      input: Tensor,
      kernel_size: tuple[int, int] | int,
      stride: int = 2,
      max_pool_size: int = 2,
      ceil_mode: bool = False,
  ) -> Tensor:
      r"""Compute pools and blurs and downsample a given feature map.
  
      .. image:: _static/img/max_blur_pool2d.png
  
      See :class:`~kornia.filters.MaxBlurPool2D` for details.
  
      Args:
          kernel_size: the kernel size for max pooling.
          stride: stride for pooling.
          max_pool_size: the kernel size for max pooling.
          ceil_mode: should be true to match output size of conv2d with same kernel size.
  
      .. note::
          This function is tested against https://github.com/adobe/antialiased-cnns.
  
      .. note::
         See a working example `here <https://kornia.github.io/tutorials/nbs/filtering_operators.html>`__.
  
      Examples:
          >>> input = torch.eye(5)[None, None]
          >>> max_blur_pool2d(input, 3)
          tensor([[[[0.5625, 0.3125],
                    [0.3125, 0.8750]]]])
      """
      KORNIA_CHECK_SHAPE(input, ["B", "C", "H", "W"])
  
      kernel = get_pascal_kernel_2d(
          kernel_size, norm=True, device=input.device, dtype=input.dtype
      ).repeat((input.shape[1], 1, 1, 1))
      return _max_blur_pool_by_kernel2d(
          input, kernel, stride, max_pool_size, ceil_mode
      )
  
imports: from __future__ import annotations
  import torch.nn.functional
  from kornia.core import Module, Tensor, as_tensor, pad, tensor
  from kornia.core.check import KORNIA_CHECK, KORNIA_CHECK_SHAPE
  from .kernels import get_pascal_kernel_2d
  from .median import _compute_zero_padding
label_desc: Use image node_id 2 for calling a global function with example usage: max_blur_pool2d(input, kernel_size, stride, max_pool_size, ceil_mode) and returns: _max_blur_pool_by_kernel2d