class_docstr: Tests that the template is compatible with all interfaces, including the computation
  of gradients.
class_docstr_tok: ["Tests","that","the","template","is","compatible","with","all","interfaces",",","including","the","computation","of","gradients","."]
docstr: Tests the torch interface.
docstr_tok: ["Tests","the","torch","interface","."]
code: def test_torch(self, tol):
      """Tests the torch interface."""
  
      import torch
  
      weights = torch.tensor(
          np.random.random(size=(6,)), requires_grad=True
      )
  
      dev = qml.device("default.qubit", wires=2)
  
      circuit = qml.QNode(circuit_template, dev)
      circuit2 = qml.QNode(circuit_decomposed, dev)
  
      res = circuit(weights)
      res2 = circuit2(weights)
      assert qml.math.allclose(res, res2, atol=tol, rtol=0)
  
      res = circuit(weights)
      res.backward()
      grads = [weights.grad]
  
      res2 = circuit2(weights)
      res2.backward()
      grads2 = [weights.grad]
  
      assert np.allclose(grads[0], grads2[0], atol=tol, rtol=0)
  
imports: import pytest
  import numpy
  import pennylane
  from pennylane import numpy
  from pennylane.templates.state_preparations.arbitrary_state_preparation import _state_preparation_pauli_words
label_desc: Use image node_id 5 for calling the TestInterfaces obj's underlying member method code with example usage: obj.test_torch(tol) without return types