docstr: Test that backpropagation derivatives work with jax with hamiltonians and large sums.
docstr_tok: ["Test","that","backpropagation","derivatives","work","with","jax","with","hamiltonians","and","large","sums","."]
code: def test_jax_backprop(self, convert_to_hamiltonian, use_jit):
      """Test that backpropagation derivatives work with jax with hamiltonians and large sums."""
      import jax
  
      jax.config.update("jax_enable_x64", True)
  
      x = jax.numpy.array(0.52, dtype=jax.numpy.float64)
      coeffs = (5.2, 6.7)
      f = (
          jax.jit(self.f, static_argnums=(1, 2, 3, 4))
          if use_jit
          else self.f
      )
  
      out = f(x, coeffs, convert_to_hamiltonian=convert_to_hamiltonian)
      expected_out = self.expected(x, coeffs)
      assert qml.math.allclose(out, expected_out)
  
      g = jax.grad(f)(
          x, coeffs, convert_to_hamiltonian=convert_to_hamiltonian
      )
      expected_g = jax.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 4 for calling the TestSumOfTermsDifferentiability obj's underlying member method code with example usage: obj.test_jax_backprop(convert_to_hamiltonian, use_jit) without return types