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 Autograd input variables.
docstr_tok: ["Test","that","the","reconstruction","of","equidistant-frequency","classical","functions","are","differentiable","for","Autograd","input","variables","."]
code: def test_differentiability_autograd(
      self, fun, spectrum, expected_grad
  ):
      """Test that the reconstruction of equidistant-frequency classical
      functions are differentiable for Autograd input variables."""
      # Convert fun to have integer frequencies
      rec = _reconstruct_gen(fun, spectrum, interface="autograd")
      grad = qml.grad(rec)
      assert fun_close(
          fun, rec, zero=pnp.array(0.0, requires_grad=True)
      )
      assert fun_close(
          expected_grad, grad, zero=pnp.array(0.0, requires_grad=True)
      )
  
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 3 for calling the TestReconstructGen obj's underlying member method code with example usage: obj.test_differentiability_autograd(fun, spectrum, expected_grad) without return types