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 a single high-dimensional tensor QNode arguments is used
docstr_tok: ["Test","that","a","gradient","transform","acts","on","QNodes","correctly","when","a","single","high-dimensional","tensor","QNode","arguments","is","used"]
code: def test_high_dimensional_single_parameter_arg(self, tol):
      """Test that a gradient transform acts on QNodes correctly
      when a single high-dimensional tensor QNode arguments is used"""
      dev = qml.device("default.qubit", wires=2)
  
      @qml.qnode(dev)
      def circuit(x):
          qml.RX(x[0, 0] / 2, wires=0)
          qml.RX(x[0, 0] / 2, wires=0)
          return qml.probs(wires=[0, 1])
  
      x = np.array([[0.1, 0.1], [0.1, 0.1]], requires_grad=True)
  
      expected = qml.jacobian(circuit)(x)
      res = qml.gradients.param_shift(circuit)(x)
      assert np.allclose(res, expected, atol=tol, rtol=0)
  
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 10 for calling the TestGradientTransformIntegration obj's underlying member method code with example usage: obj.test_high_dimensional_single_parameter_arg(tol) and returns: qml