class_docstr: Test the state_vector() method of various state-prep operations.
class_docstr_tok: ["Test","the","state_vector","(",")","method","of","various","state-prep","operations","."]
docstr: Test that StatePrep works with torch.
docstr_tok: ["Test","that","StatePrep","works","with","torch","."]
code: def test_StatePrep_torch_differentiable(self):
      """Test that StatePrep works with torch."""
      import torch
  
      def QuantumLayer():
          @qml.qnode(qml.device("default.qubit"), interface="torch")
          def qlayer(inputs, weights):
              qml.StatePrep(inputs, wires=[1, 2, 3])
              qml.RY(phi=weights, wires=[0])
              return qml.expval(qml.PauliZ(wires=0))
  
          weight_shapes = {"weights": (1)}
          return qml.qnn.TorchLayer(qlayer, weight_shapes)
  
      class SimpleQuantumModel(
          torch.nn.Module
      ):  # pylint:disable=too-few-public-methods
          def __init__(self):
              super().__init__()
              self.quantum_layer = QuantumLayer()
  
          def forward(self, x):
              return self.quantum_layer(x)
  
      model = SimpleQuantumModel()
      features = torch.tensor(
          [
              [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
              [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
          ],
          requires_grad=True,
      )
      result = model(features)
      assert qml.math.get_interface(result) == "torch"
      assert qml.math.shape(result) == (2,)
  
imports: import pytest
  import pennylane
  from pennylane import numpy
  from pennylane.wires import WireError
label_desc: Use image node_id 10 for calling the TestStateVector obj's underlying member method code with example usage: obj.test_StatePrep_torch_differentiable() and returns: qml, qml, self