class_docstr: Test that the supported configurations in the documentation
  matches the supported configurations in the code.
  
  Also test that the supported configurations do not raise any errors and
  the unsupported configurations raise the expected errors or otherwise
  behave as expected.
  
  These tests do not test for correctness.
class_docstr_tok: ["Test","that","the","supported","configurations","in","the","documentation","matches","the","supported","configurations","in","the","code",".","Also","test","that","the","supported","configurations","do","not","raise","any","errors","and","the","unsupported","configurations","raise","the","expected","errors","or","otherwise","behave","as","expected",".","These","tests","do","not","test","for","correctness","."]
docstr: Test gradient of state directly succeeds for non-autograd interfaces
docstr_tok: ["Test","gradient","of","state","directly","succeeds","for","non-autograd","interfaces"]
code: def test_all_state_backprop(self, interface, wire_specs):
      """Test gradient of state directly succeeds for non-autograd interfaces"""
      circuit = get_qnode(
          interface, "backprop", "StateVector", None, wire_specs
      )
      x = get_variable(interface, wire_specs, complex=True)
      if interface == "torch":
          pytest.xfail(reason="see pytorch/pytorch/issues/94397")
      compute_gradient(
          x, interface, circuit, "StateVector", complex=True
      )
  
imports: import pytest
  import pennylane
  from pennylane import numpy
  from pennylane import QuantumFunctionError
  from pennylane.measurements import Probability, Expectation, Variance, Sample, VnEntropy, MutualInfo
  from pennylane.measurements.measurements import ObservableReturnTypes
label_desc: Use image node_id 15 for calling the TestSupportedConfs obj's underlying member method code with example usage: obj.test_all_state_backprop(interface, wire_specs) without return types