class_docstr: Tests that grouping observables is differentiable with respect to the coefficients.
class_docstr_tok: ["Tests","that","grouping","observables","is","differentiable","with","respect","to","the","coefficients","."]
docstr: Test that grouping is differentiable with autograd tensors as coefficient
docstr_tok: ["Test","that","grouping","is","differentiable","with","autograd","tensors","as","coefficient"]
code: def test_differentiation_autograd(self, tol):
      """Test that grouping is differentiable with autograd tensors as coefficient"""
      coeffs = pnp.array([1.0, 2.0, 3.0], requires_grad=True)
      obs = [PauliX(wires=0), PauliX(wires=1), PauliZ(wires=1)]
  
      def group(coeffs, select=None):
          _, grouped_coeffs = group_observables(obs, coeffs)
          return grouped_coeffs[select]
  
      jac_fn = qml.jacobian(group)
      assert pnp.allclose(
          jac_fn(coeffs, select=0),
          pnp.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0]]),
          atol=tol,
      )
      assert pnp.allclose(
          jac_fn(coeffs, select=1),
          pnp.array([[0.0, 0.0, 1.0]]),
          atol=tol,
      )
  
imports: import pytest
  import numpy
  import pennylane
  from pennylane import Identity, PauliX, PauliY, PauliZ
  from pennylane.pauli import are_identical_pauli_words
  from pennylane.pauli.grouping.group_observables import PauliGroupingStrategy, group_observables
  from pennylane import numpy
label_desc: Use image node_id 1 for calling the TestDifferentiable obj's underlying member method code with example usage: obj.test_differentiation_autograd(tol) and returns: grouped_coeffs