code: def laplacian(x: FourierSeries, *, params=None):
      r"""Laplacian operator for Fourier series fields.
  
      Args:
        x (FourierSeries): Input field
  
      Returns:
        The Laplacian of the field
      """
      assert x.dims == 1  # Laplacian only defined for scalar fields
  
      k_vec = params["k_vec"]
      u = x.params[..., 0]
  
      def single_grad(axis, u):
          u = jnp.moveaxis(u, axis, -1)
          Fx = x._ffts[0](u, axis=-1)
          iku = -Fx * (k_vec[axis] ** 2)
          du = x._ffts[1](iku, axis=-1, n=u.shape[-1])
          return jnp.moveaxis(du, -1, axis)
  
      new_params = jnp.sum(
          jnp.stack(
              [single_grad(i, u) for i in range(x.domain.ndim)], axis=-1
          ),
          axis=-1,
          keepdims=True,
      )
      return FourierSeries(new_params, x.domain)
  
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 16 for calling a global function with example usage: laplacian(x) and returns: FourierSeries, jnp