class_docstr: Tests for parameter gradient methods
class_docstr_tok: ["Tests","for","parameter","gradient","methods"]
docstr: In non-graph mode, it is impossible to determine
  if a parameter is independent or not
docstr_tok: ["In","non-graph","mode",",","it","is","impossible","to","determine","if","a","parameter","is","independent","or","not"]
code: def test_independent_no_graph_mode(self):
      """In non-graph mode, it is impossible to determine
      if a parameter is independent or not"""
  
      with qml.queuing.AnnotatedQueue() as q:
          qml.RX(0.543, wires=[0])
          qml.RY(-0.654, wires=[1])
          qml.expval(qml.PauliY(0))
  
      tape = qml.tape.QuantumScript.from_queue(q)
      trainable_params = choose_trainable_params(tape, None)
      diff_methods = _find_gradient_methods(
          tape, trainable_params, use_graph=False
      )
  
      assert diff_methods[0] == "A"
      assert diff_methods[1] == "A"
  
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 3 for calling the TestGradAnalysis obj's underlying member method code with example usage: obj.test_independent_no_graph_mode() without return types