code: def fd_laplacian_init(x: FiniteDifferences, *args, **kwargs):
      r"""Initializes the parameters for the Laplacian of a FiniteDifferences field. Accepts
      an arbitrary number of positional and keyword arguments after the
      mandatory arguments, which are ignored.
  
      Args:
        x: FiniteDifferences field
  
      Returns:
        Stencil coefficients
  
      """
  
      # Get kernel for 1D
      kernel = get_fd_coefficients(x, order=2)
  
      # Make it into an ND kernel by repeating it
      if x.domain.ndim == 1:
          return kernel
  
      full_kernel_shape = [kernel.shape[0]] * x.domain.ndim
      full_kernel = np.zeros(full_kernel_shape, dtype=kernel.dtype)
      center = kernel.shape[0] // 2
  
      # Put the kernel in the middle of the array for each dimension, by adding it to the full kernel
      for i in range(x.domain.ndim):
          full_kernel[
              tuple(
                  [
                      center if j == i else slice(None)
                      for j in range(x.domain.ndim)
                  ]
              )
          ] += kernel
  
      return full_kernel
  
imports: from typing import Union
  import jax
  import numpy
  from jax import numpy
  from jaxdf.conv import fd_coefficients_fornberg, reflection_conv
  from jaxdf.core import operator
  from jaxdf.discretization import Continuous, FiniteDifferences, FourierSeries
label_desc: Use image node_id 17 for calling a global function with example usage: fd_laplacian_init(x) and returns: full_kernel, kernel