class_docstr: Tests that the transforms are differentiable if the inner execution is differentiable.
  
  Note that testing is only done for the required method for each ml framework.
class_docstr_tok: ["Tests","that","the","transforms","are","differentiable","if","the","inner","execution","is","differentiable",".","Note","that","testing","is","only","done","for","the","required","method","for","each","ml","framework","."]
docstr: Test that the derivatives of compute_vjp can be taken with tensorflow.
docstr_tok: ["Test","that","the","derivatives","of","compute_vjp","can","be","taken","with","tensorflow","."]
code: def test_vjp_tf(self):
      """Test that the derivatives of compute_vjp can be taken with tensorflow."""
  
      import tensorflow as tf
  
      jpc = param_shift_jpc
  
      def f(x, dy):
          tape = qml.tape.QuantumScript(
              [qml.RX(x, 0)], [qml.expval(qml.PauliZ(0))]
          )
          vjp = jpc.compute_vjp((tape,), (dy,))
          return vjp[0]
  
      x = tf.Variable(0.6, dtype=tf.float64)
      dy = tf.Variable(1.5, dtype=tf.float64)
  
      with tf.GradientTape() as tape:
          res = f(x, dy)
  
      assert qml.math.allclose(res, -1.5 * np.sin(0.6))
  
      dx, ddy = tape.gradient(res, (x, dy))
  
      assert qml.math.allclose(dx, -1.5 * np.cos(0.6))
      assert qml.math.allclose(ddy, -np.sin(0.6))
  
imports: import pytest
  from cachetools import LRUCache
  from param_shift_dev import ParamShiftDerivativesDevice
  import numpy
  import pennylane
  from pennylane.workflow.jacobian_products import JacobianProductCalculator, TransformJacobianProducts, DeviceDerivatives, DeviceJacobianProducts, LightningVJPs
label_desc: Use image node_id 4 for calling the TestTransformsDifferentiability obj's underlying member method code with example usage: obj.test_vjp_tf() and returns: vjp