class_docstr: Tests for the finite difference gradient transform
class_docstr_tok: ["Tests","for","the","finite","difference","gradient","transform"]
docstr: Test that the correct ouput and warning is generated in the absence of any trainable
  parameters
code: def test_no_trainable_params_qnode_jax(self):
      """Test that the correct ouput and warning is generated in the absence of any trainable
      parameters"""
      dev = qml.device("default.qubit", wires=2)
  
      @qml.qnode(dev, interface="jax")
      def circuit(weights):
          qml.RX(weights[0], wires=0)
          qml.RY(weights[1], wires=0)
          return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1))
  
      weights = [0.1, 0.2]
      with pytest.raises(
          qml.QuantumFunctionError, match="No trainable parameters."
      ):
          qml.gradients.finite_diff(circuit)(weights)
  
imports: import numpy
  import pytest
  import pennylane
  from pennylane import numpy
  from pennylane.devices import DefaultQubitLegacy
  from pennylane.gradients import finite_diff, finite_diff_coeffs
  from pennylane.operation import AnyWires, Observable
label_desc: Use image node_id 9 for calling the TestFiniteDiff obj's underlying member method code with example usage: obj.test_no_trainable_params_qnode_jax() and returns: qml