class_docstr: Test that the gradient transforms are differentiable
  using each interface
class_docstr_tok: ["Test","that","the","gradient","transforms","are","differentiable","using","each","interface"]
docstr: Test that a gradient transform remains differentiable
  with TF
docstr_tok: ["Test","that","a","gradient","transform","remains","differentiable","with","TF"]
code: def test_tf(self, tol):
      """Test that a gradient transform remains differentiable
      with TF"""
      import tensorflow as tf
  
      dev = qml.device("default.qubit", wires=2)
  
      @qml.gradients.param_shift
      @qml.qnode(dev, interface="tf", diff_method="parameter-shift")
      def circuit(x):
          qml.RY(x**2, wires=[1])
          qml.CNOT(wires=[0, 1])
          return qml.var(qml.PauliX(1))
  
      x_ = -0.654
      x = tf.Variable(x_, dtype=tf.float64)
  
      with tf.GradientTape() as tape:
          res = circuit(x)
  
      expected = -4 * x_ * np.cos(x_**2) * np.sin(x_**2)
      assert np.allclose(res, expected, atol=tol, rtol=0)
  
      res = tape.gradient(res, x)
      expected = -2 * (
          4 * x_**2 * np.cos(2 * x_**2) + np.sin(2 * x_**2)
      )
      assert np.allclose(res, expected, atol=tol, rtol=0)
  
imports: import pytest
  import pennylane
  from pennylane import numpy
  from pennylane.gradients.gradient_transform import _find_gradient_methods, _validate_gradient_methods, choose_trainable_params
label_desc: Use image node_id 2 for calling the TestInterfaceIntegration obj's underlying member method code with example usage: obj.test_tf(tol) and returns: qml