class_docstr: Test for caching behaviour
class_docstr_tok: ["Test","for","caching","behaviour"]
docstr: Test that, with the parameter-shift transform,
  Torch always uses the optimum number of evals when computing the Jacobian.
code: def test_caching_param_shift(self):
      """Test that, with the parameter-shift transform,
      Torch always uses the optimum number of evals when computing the Jacobian.
      """
      dev = qml.device("default.qubit.legacy", wires=1)
  
      def cost(a, cache):
          with qml.queuing.AnnotatedQueue() as q:
              qml.RY(a[0], wires=0)
              qml.RX(a[1], wires=0)
              qml.probs(wires=0)
  
          tape = qml.tape.QuantumScript.from_queue(q)
  
          return execute(
              [tape],
              dev,
              gradient_fn=param_shift,
              cache=cache,
              interface="torch",
          )[0][0]
  
      # Without caching, 5 evaluations are required to compute
      # the Jacobian: 1 (forward pass) + (2 shifts * 2 params)
      params = torch.tensor([0.1, 0.2], requires_grad=True)
      torch_functional.jacobian(lambda p: cost(p, cache=None), params)
      assert dev.num_executions == 5
  
      # With caching, 5 evaluations are required to compute
      # the Jacobian: 1 (forward pass) + (2 shifts * 2 params)
      dev._num_executions = 0
      torch_functional.jacobian(lambda p: cost(p, cache=True), params)
      assert dev.num_executions == 5
  
imports: import numpy
  import pytest
  import pennylane
  from pennylane.gradients import finite_diff, param_shift
  from pennylane import execute
label_desc: Use image node_id 3 for calling the TestCaching obj's underlying member method code with example usage: obj.test_caching_param_shift() and returns: execute