class_docstr: Class to test the shape of the Grad/Jacobian/Hessian with different return types.
class_docstr_tok: ["Class","to","test","the","shape","of","the","Grad\/Jacobian\/Hessian","with","different","return","types","."]
docstr: For a multi dimensional measurement (probs), check that a single array is returned with the correct
  dimension
docstr_tok: ["For","a","multi","dimensional","measurement","(","probs",")",",","check","that","a","single","array","is","returned","with","the","correct","dimension"]
code: def test_jacobian_single_measurement_param_probs(
      self, dev, diff_method, grad_on_execution, device_vjp, shots
  ):
      """For a multi dimensional measurement (probs), check that a single array is returned with the correct
      dimension"""
      if shots is not None and diff_method in ("backprop", "adjoint"):
          pytest.skip(
              "Test does not support finite shots and adjoint/backprop"
          )
  
      if diff_method == "adjoint":
          pytest.skip(
              "Test does not supports adjoint because of probabilities."
          )
  
      @qnode(
          dev,
          interface="torch",
          diff_method=diff_method,
          device_vjp=device_vjp,
          grad_on_execution=grad_on_execution,
      )
      def circuit(a):
          qml.RY(a, wires=0)
          qml.RX(0.2, wires=0)
          return qml.probs(wires=[0, 1])
  
      a = torch.tensor(0.1, requires_grad=True)
  
      jac = jacobian(circuit, a)
  
      assert isinstance(jac, torch.Tensor)
      assert jac.shape == (4,)
  
imports: import numpy
  import pytest
  import pennylane
  from pennylane import qnode
  from pennylane.devices import DefaultQubit
label_desc: Use image node_id 4 for calling the TestReturn obj's underlying member method code with example usage: obj.test_jacobian_single_measurement_param_probs(dev, diff_method, grad_on_execution, device_vjp, shots) and returns: qml