class_docstr: Autodiff tests for all frameworks
class_docstr_tok: ["Autodiff","tests","for","all","frameworks"]
docstr: Test that results after splitting are still differentiable with tf
docstr_tok: ["Test","that","results","after","splitting","are","still","differentiable","with","tf"]
code: def test_split_with_tf_probs(self):
      """Test that results after splitting are still differentiable with tf"""
  
      import tensorflow as tf
  
      dev = qml.device("default.qubit.tf", wires=2)
  
      @qml.qnode(dev)
      def circuit(params):
          qml.RX(params[0], wires=0)
          qml.RY(params[1], wires=1)
          return (
              qml.probs(wires=[0, 1]),
              qml.expval(qml.PauliX(0) @ qml.PauliX(1)),
          )
  
      params = tf.Variable([0.5, 0.5])
      with tf.GradientTape() as tape:
          res = split_non_commuting(circuit)(params)
          res = tf.concat([res[0]] + [tf.stack(res[1:])], axis=0)
  
      grad = tape.jacobian(res, params)
      assert all(np.isclose(res, exp_res_probs))
      assert all(np.isclose(grad, exp_grad_probs, atol=1e-5).flatten())
  
imports: import itertools
  import pytest
  import numpy
  import pennylane
  import pennylane.numpy
  from pennylane.transforms import split_non_commuting
label_desc: Use image node_id 12 for calling the TestAutodiffSplitNonCommuting obj's underlying member method code with example usage: obj.test_split_with_tf_probs() and returns: qml,  qml