docstr: Test that backpropagation derivatives work with tensorflow with hamiltonians and large sums.
docstr_tok: ["Test","that","backpropagation","derivatives","work","with","tensorflow","with","hamiltonians","and","large","sums","."]
code: def test_tf_backprop(self, convert_to_hamiltonian):
      """Test that backpropagation derivatives work with tensorflow with hamiltonians and large sums."""
      import tensorflow as tf
  
      x = tf.Variable(0.5)
      coeffs = [8.3, 5.7]
  
      with tf.GradientTape() as tape1:
          out = self.f(
              x, coeffs, convert_to_hamiltonian=convert_to_hamiltonian
          )
  
      with tf.GradientTape() as tape2:
          expected_out = self.expected(x, coeffs)
  
      assert qml.math.allclose(out, expected_out)
      g1 = tape1.gradient(out, x)
      g2 = tape2.gradient(expected_out, x)
      assert qml.math.allclose(g1, g2)
  
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 6 for calling the TestSumOfTermsDifferentiability obj's underlying member method code with example usage: obj.test_tf_backprop(convert_to_hamiltonian) without return types