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: The jacobian of multiple measurements with a multiple params array return a single array.
docstr_tok: ["The","jacobian","of","multiple","measurements","with","a","multiple","params","array","return","a","single","array","."]
code: def test_jacobian_multiple_measurement_multiple_param_array(
      self, dev, diff_method, grad_on_execution, device_vjp, shots
  ):
      """The jacobian of multiple measurements with a multiple params array return a single array."""
      if diff_method == "adjoint":
          pytest.skip(
              "Test does not supports adjoint because of probabilities."
          )
      if shots is not None and diff_method in ("backprop", "adjoint"):
          pytest.skip(
              "Test does not support finite shots and adjoint/backprop"
          )
  
      @qnode(
          dev,
          interface="torch",
          diff_method=diff_method,
          grad_on_execution=grad_on_execution,
          device_vjp=device_vjp,
      )
      def circuit(a):
          qml.RY(a[0], wires=0)
          qml.RX(a[1], wires=0)
          return qml.expval(qml.PauliZ(0)), qml.probs(wires=[0, 1])
  
      a = torch.tensor([0.1, 0.2], requires_grad=True)
  
      jac = jacobian(lambda _a: circuit(_a, shots=shots), a)
  
      assert isinstance(jac, tuple)
      assert len(jac) == 2  # measurements
  
      assert isinstance(jac[0], torch.Tensor)
      assert jac[0].shape == (2,)
  
      assert isinstance(jac[1], torch.Tensor)
      assert jac[1].shape == (4, 2)
  
imports: import numpy
  import pytest
  import pennylane
  from pennylane import qnode
  from pennylane.devices import DefaultQubit
label_desc: Use image node_id 13 for calling the TestReturn obj's underlying member method code with example usage: obj.test_jacobian_multiple_measurement_multiple_param_array(dev, diff_method, grad_on_execution, device_vjp, shots) and returns: qml,  qml