class_docstr: Tests for parameter gradient methods
docstr: Probability is the expectation value of a
  higher order observable, and thus only supports numerical
  differentiation
docstr_tok: ["Probability","is","the","expectation","value","of","a","higher","order","observable",",","and","thus","only","supports","numerical","differentiation"]
code: def test_probability(self):
      """Probability is the expectation value of a
      higher order observable, and thus only supports numerical
      differentiation"""
      with qml.queuing.AnnotatedQueue() as q:
          qml.Rotation(0.543, wires=[0])
          qml.Squeezing(0.543, 0, wires=[0])
          qml.probs(wires=0)
  
      tape = qml.tape.QuantumScript.from_queue(q)
      assert _grad_method_cv(tape, 0) == "F"
      assert _grad_method_cv(tape, 1) == "F"
      assert _grad_method_cv(tape, 2) == "F"
  
imports: import unittest.mock
  import pytest
  import pennylane
  from pennylane import numpy
  from pennylane.gradients import param_shift_cv
  from pennylane.gradients.gradient_transform import choose_trainable_params
  from pennylane.gradients.parameter_shift_cv import _grad_method_cv, _find_gradient_methods_cv, _transform_observable
label_desc: Use image node_id 5 for calling the TestGradAnalysis obj's underlying member method code with example usage: obj.test_probability() without return types