code: def test_finite_difference_laplacian():
      domain = geometry.Domain((11,), (0.5,))
      params = jnp.zeros((11, 1))
      params = params.at[5].set(1.0)
      u = FiniteDifferences(params, domain, accuracy=4)
      lap_u = operators.laplacian(u)
  
      # If you want to print the kernel of the laplacian operator (similar to the gradient test):
      kernel = operators.laplacian.default_params(u)
  
      grid_values = lap_u.on_grid[:, 0]
      true_values = jnp.asarray(
          [
              0,
              0.0,
              0.0,
              -0.08333334,
              1.3333334,
              -2.5,
              1.3333334,
              -0.08333334,
              0.0,
              0.0,
              0.0,
          ]
      )
      assert jnp.allclose(grid_values, true_values)
  
file_path: jaxdf/tests/test_differential.py
imports: import jax
  from jax import jit, make_jaxpr
  from jax import numpy
  from jaxdf import *
label_desc: Use image node_id 10 for calling a global function with example usage: test_finite_difference_laplacian() without return types