class_docstr: Tests the one-dimensional reconstruction subroutine based on arbitrary
  shifted evaluations for arbitrary frequencies.
docstr: Test that the reconstruction of equidistant-frequency classical
  functions are differentiable for JAX input variables.
docstr_tok: ["Test","that","the","reconstruction","of","equidistant-frequency","classical","functions","are","differentiable","for","JAX","input","variables","."]
code: def test_differentiability_jax(self, fun, spectrum, expected_grad):
      """Test that the reconstruction of equidistant-frequency classical
      functions are differentiable for JAX input variables."""
      import jax
  
      # Convert fun to have integer frequencies
      rec = _reconstruct_gen(fun, spectrum, interface="jax")
      grad = jax.grad(rec)
      assert fun_close(fun, rec, zero=jax.numpy.array(0.0))
      assert fun_close(expected_grad, grad, zero=jax.numpy.array(0.0))
  
imports: from inspect import signature
  from itertools import chain
  from functools import reduce
  import pytest
  import numpy
  import pennylane
  from pennylane import numpy
  from pennylane.fourier.reconstruct import _reconstruct_equ, _reconstruct_gen, _prepare_jobs, reconstruct
  from pennylane.fourier.utils import join_spectra
label_desc: Use image node_id 4 for calling the TestReconstructGen obj's underlying member method code with example usage: obj.test_differentiability_jax(fun, spectrum, expected_grad) without return types