code: def test_for_grad(
      self, f, ref, body_shapes, n, for_impl, for_body_name, impl_name
  ):
      for_ = for_impl
      rng = self.rng()
  
      args = [rng.randn(*s) for s in body_shapes]
  
      tol = {np.float64: 1e-12, np.float32: 1e-4}
      ans = jax.grad(lambda args: for_(n, f, args)[1].sum())(args)
      ans_discharged = jax.grad(
          lambda args: for_reference(n, f, args)[1].sum()
      )(args)
      expected = jax.grad(lambda args: ref(*args)[1].sum())(args)
      self.assertAllClose(
          ans, ans_discharged, check_dtypes=True, rtol=tol, atol=tol
      )
      self.assertAllClose(
          ans, expected, check_dtypes=True, rtol=tol, atol=tol
      )
      jtu.check_grads(
          lambda *args: for_(n, f, args)[1].sum(),
          args,
          order=2,
          rtol=7e-3,
          atol=1e-2,
      )
  
imports: from functools import partial
  from absl.testing import absltest
  from absl.testing import parameterized
  import numpy
  import jax
  from jax import random
  from jax._src import test_util
  from jax._src.lax.control_flow import for_loop
  import jax.numpy
  from jax import config
label_desc: Use image node_id 5 for calling the ForLoopTransformationTest obj's underlying member method code with example usage: obj.test_for_grad(f, ref, body_shapes, n, for_impl, for_body_name, impl_name) without return types