code: def test_fourier_gradient():
      _ = m.params[..., 0]
      z = operators.gradient(m)
      _ = z.params[..., 0]
      _ = z.params[..., 1]
  
      @jit
      def f(m):
          return operators.gradient(m)
  
      _ = f(m).params[..., 0]
      _ = f(m).params[..., 1]
  
      _ = f(a)
      _ = make_jaxpr(f)(a)
      _ = make_jaxpr(f)(m)
  
      op_params = operators.gradient.default_params(m)
      _ = ("op_params", op_params)
      z = operators.gradient(m, params=op_params)
      _ = z.params[..., 0]
  
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 2 for calling a global function with example usage: test_fourier_gradient() and returns: operators