code: def build_laplacian_pyramid(
      input: Tensor,
      max_level: int,
      border_type: str = "reflect",
      align_corners: bool = False,
  ) -> list[Tensor]:
      r"""Construct the Laplacian pyramid for a tensor image.
  
      The function constructs a vector of images and builds the Laplacian pyramid
      by recursively computing the difference after applying
      pyrUp to the adjacent layer in its Gaussian pyramid.
  
      See :cite:`burt1987laplacian` for more details.
  
      Args:
          input : the tensor to be used to construct the pyramid with shape :math:`(B, C, H, W)`.
          max_level: 0-based index of the last (the smallest) pyramid layer.
            It must be non-negative.
          border_type: the padding mode to be applied before convolving.
            The expected modes are: ``'constant'``, ``'reflect'``,
            ``'replicate'`` or ``'circular'``.
          align_corners: interpolation flag.
  
      Return:
          Output: :math:`[(B, C, H, W), (B, C, H/2, W/2), ...]`
      """
  
      KORNIA_CHECK_SHAPE(input, ["B", "C", "H", "W"])
      KORNIA_CHECK(
          isinstance(max_level, int) or max_level < 0,
          f"Invalid max_level, it must be a positive integer. Got: {max_level}",
      )
  
      h = input.size()[2]
      w = input.size()[3]
      require_padding = not (is_powerof_two(w) or is_powerof_two(h))
  
      if require_padding:
          # in case of arbitrary shape tensor image need to be padded.
          # Reference: https://stackoverflow.com/a/29967555
          padding = (
              0,
              find_next_powerof_two(w) - w,
              0,
              find_next_powerof_two(h) - h,
          )
          input = pad(input, padding, "reflect")
  
      # create gaussian pyramid
      gaussian_pyramid: list[Tensor] = build_pyramid(
          input, max_level, border_type, align_corners
      )
      # create empty list
      laplacian_pyramid: list[Tensor] = []
  
      # iterate and compute difference of adjacent layers in a gaussian pyramid
      for i in range(max_level - 1):
          img_expand: Tensor = pyrup(
              gaussian_pyramid[i + 1], border_type, align_corners
          )
          laplacian: Tensor = gaussian_pyramid[i] - img_expand
          laplacian_pyramid.append(laplacian)
      laplacian_pyramid.append(gaussian_pyramid[-1])
      return laplacian_pyramid
  
imports: from __future__ import annotations
  import math
  import torch
  import torch.nn.functional
  from kornia.core import Module, Tensor, ones, pad, stack, tensor, zeros
  from kornia.core.check import KORNIA_CHECK, KORNIA_CHECK_IS_TENSOR, KORNIA_CHECK_SHAPE
  from kornia.filters import filter2d, gaussian_blur2d
label_desc: Use image node_id 7 for calling a global function with example usage: build_laplacian_pyramid(input, max_level, border_type, align_corners) and returns: laplacian_pyramid