docstr: Test that backpropagation derivatives work with torch with hamiltonians and large sums.
docstr_tok: ["Test","that","backpropagation","derivatives","work","with","torch","with","hamiltonians","and","large","sums","."]
code: def test_torch_backprop(self, convert_to_hamiltonian):
      """Test that backpropagation derivatives work with torch with hamiltonians and large sums."""
      import torch
  
      coeffs = [
          torch.tensor(9.2, requires_grad=False, dtype=torch.float64),
          torch.tensor(6.2, requires_grad=False, dtype=torch.float64),
      ]
  
      x = torch.tensor(-0.289, requires_grad=True, dtype=torch.float64)
      x2 = torch.tensor(-0.289, requires_grad=True, dtype=torch.float64)
      out = self.f(
          x, coeffs, convert_to_hamiltonian=convert_to_hamiltonian
      )
      expected_out = self.expected(x2, coeffs, like="torch")
      assert qml.math.allclose(out, expected_out)
  
      out.backward()
      expected_out.backward()
      assert qml.math.allclose(x.grad, x2.grad)
  
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 5 for calling the TestSumOfTermsDifferentiability obj's underlying member method code with example usage: obj.test_torch_backprop(convert_to_hamiltonian) without return types