class_docstr: Tests for the qfunc_transform decorator differentiability
class_docstr_tok: ["Tests","for","the","qfunc_transform","decorator","differentiability"]
docstr: Test that a qfunc transform is differentiable when using
  TensorFlow
docstr_tok: ["Test","that","a","qfunc","transform","is","differentiable","when","using","TensorFlow"]
code: def test_differentiable_qfunc_tf(self, diff_method):
      """Test that a qfunc transform is differentiable when using
      TensorFlow"""
      import tensorflow as tf
  
      dev = qml.device("default.qubit", wires=2)
      qnode = qml.QNode(self.circuit, dev, diff_method=diff_method)
  
      a_np = np.array(0.5, requires_grad=True)
      b_np = np.array([0.1, 0.2], requires_grad=True)
      x_np = np.array(0.543, requires_grad=True)
      a = tf.Variable(a_np, dtype=tf.float64)
      b = tf.Variable(b_np, dtype=tf.float64)
      x = tf.Variable(x_np, dtype=tf.float64)
  
      with tf.GradientTape() as tape:
          res = qnode(x, a, b)
  
      assert np.allclose(res, self.expval(x, a, b))
  
      grad = tape.gradient(res, [x, a, b])
      expected = qml.grad(self.expval)(x_np, a_np, b_np)
      assert all(np.allclose(g, e) for g, e in zip(grad, expected))
  
imports: import pytest
  import pennylane
  from pennylane import numpy
label_desc: Use image node_id 6 for calling the TestQFuncTransformGradients obj's underlying member method code with example usage: obj.test_differentiable_qfunc_tf(diff_method) without return types