class_docstr: Test that tapes ending with expectations
  of Hamiltonians provide correct results and gradients
class_docstr_tok: ["Test","that","tapes","ending","with","expectations","of","Hamiltonians","provide","correct","results","and","gradients"]
code: def test_multiple_hamiltonians_trainable(
      self, cost_fn, execute_kwargs, tol
  ):
      # pylint: disable=unused-argument
      coeffs1 = torch.tensor(
          [0.1, 0.2, 0.3], requires_grad=True, dtype=torch.float64
      )
      coeffs2 = torch.tensor(
          [0.7], requires_grad=True, dtype=torch.float64
      )
      weights = torch.tensor(
          [0.4, 0.5], requires_grad=True, dtype=torch.float64
      )
      dev = qml.device("default.qubit.legacy", wires=2)
  
      res = cost_fn(weights, coeffs1, coeffs2, dev=dev)
      expected = self.cost_fn_expected(weights, coeffs1, coeffs2)
      assert np.allclose(res[0].detach(), expected[0], atol=tol, rtol=0)
      assert np.allclose(res[1].detach(), expected[1], atol=tol, rtol=0)
  
      res = torch.hstack(
          torch_functional.jacobian(
              lambda *x: cost_fn(*x, dev=dev),
              (weights, coeffs1, coeffs2),
          )
      )
      expected = self.cost_fn_jacobian(weights, coeffs1, coeffs2)
      assert np.allclose(res.detach(), expected, atol=tol, rtol=0)
  
imports: import numpy
  import pytest
  import pennylane
  from pennylane.gradients import finite_diff, param_shift
  from pennylane import execute
label_desc: Use image node_id 5 for calling the TestHamiltonianWorkflows obj's underlying member method code with example usage: obj.test_multiple_hamiltonians_trainable(cost_fn, execute_kwargs, tol) without return types