docstr: Test that backpropagation derivatives work in autograd with hamiltonians and large sums.
docstr_tok: ["Test","that","backpropagation","derivatives","work","in","autograd","with","hamiltonians","and","large","sums","."]
code: def test_autograd_backprop(self, convert_to_hamiltonian, coeffs):
      """Test that backpropagation derivatives work in autograd with hamiltonians and large sums."""
      x = qml.numpy.array(0.52)
      out = self.f(
          x, coeffs, convert_to_hamiltonian=convert_to_hamiltonian
      )
      expected_out = self.expected(x, coeffs)
      assert qml.math.allclose(out, expected_out)
  
      g = qml.grad(self.f)(
          x, coeffs, convert_to_hamiltonian=convert_to_hamiltonian
      )
      expected_g = qml.grad(self.expected)(x, coeffs)
      assert qml.math.allclose(g, expected_g)
  
imports: import pytest
  import numpy
  from scipy.sparse import csr_matrix
  import pennylane
  from pennylane.devices.qubit import simulate
  from pennylane.devices.qubit.measure import measure, state_diagonalizing_gates, csr_dot_products, full_dot_products, get_measurement_function, sum_of_terms_method
label_desc: Use image node_id 3 for calling the TestSumOfTermsDifferentiability obj's underlying member method code with example usage: obj.test_autograd_backprop(convert_to_hamiltonian, coeffs) without return types