class_docstr: Testing differentiability of classical fisher info matrix (CFIM)
class_docstr_tok: ["Testing","differentiability","of","classical","fisher","info","matrix","(","CFIM",")"]
docstr: Testing diffability with an analytic example for tf. The CFIM of this single
  qubit is constant, so the gradient should be zero.
docstr_tok: ["Testing","diffability","with","an","analytic","example","for","tf",".","The","CFIM","of","this","single","qubit","is","constant",",","so","the","gradient","should","be","zero","."]
code: def test_diffability_tf(self):
      """Testing diffability with an analytic example for tf. The CFIM of this single
      qubit is constant, so the gradient should be zero."""
      import tensorflow as tf
  
      dev = qml.device("default.qubit", wires=1)
  
      @qml.qnode(dev)
      def circ(params):
          qml.RY(params, wires=0)
          return qml.probs(wires=range(1))
  
      params = tf.Variable(np.pi / 4)
  
      assert np.allclose(qml.qinfo.classical_fisher(circ)(params), 1)
  
      result = np.zeros((1, 1, 1), dtype="float64")
      with tf.GradientTape() as tape:
          loss = qml.qinfo.classical_fisher(circ)(params)
  
      result_calc = tape.jacobian(loss, params)
  
      assert np.allclose(result, result_calc, atol=1e-6)
  
imports: import pytest
  import numpy
  import pennylane
  import pennylane.numpy
  from pennylane.qinfo import classical_fisher, quantum_fisher
  from pennylane.qinfo.transforms import _make_probs, _compute_cfim
label_desc: Use image node_id 3 for calling the TestDiffCFIM obj's underlying member method code with example usage: obj.test_diffability_tf() and returns: qml