class_docstr: Test integration of the gradient transform decorator
class_docstr_tok: ["Test","integration","of","the","gradient","transform","decorator"]
docstr: Test that a gradient transform acts on QNodes
  correctly when multiple tensor QNode arguments are present
docstr_tok: ["Test","that","a","gradient","transform","acts","on","QNodes","correctly","when","multiple","tensor","QNode","arguments","are","present"]
code: def test_multiple_tensor_arguments(self, tol):
      """Test that a gradient transform acts on QNodes
      correctly when multiple tensor QNode arguments are present"""
      dev = qml.device("default.qubit", wires=2)
  
      @qml.qnode(dev)
      def circuit(x, y):
          qml.RX(x[0, 0], wires=0)
          qml.RY(y[0, 0], wires=0)
          qml.RZ(x[1, 0], wires=0)
          return qml.probs(wires=[0, 1])
  
      x = np.array([[0.1, 0.3], [0.2, -0.1]], requires_grad=True)
      y = np.array([[0.2, 0.2], [0.3, 0.5]], requires_grad=True)
  
      expected = qml.jacobian(circuit)(x, y)
      # pylint:disable=unexpected-keyword-arg
      res = qml.gradients.param_shift(circuit)(x, y)
      assert isinstance(res, tuple) and len(res) == 2
      assert all(
          np.allclose(_r, _e, atol=tol, rtol=0)
          for _r, _e in zip(res, expected)
      )
  
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 8 for calling the TestGradientTransformIntegration obj's underlying member method code with example usage: obj.test_multiple_tensor_arguments(tol) and returns: qml