code: def test_call_and_grad_approximate_softmax(self):
      layer_partial = tl.Serial(
          tl.Branch(tl.Embedding(11, 12), tl.PaddingMask()),
          sparsity.Favor(
              d_feature=12,
              n_heads=3,
              n_random_features=128,
              use_approximate_softmax=True,
          ),
          tl.Select([0], n_in=2),
      )
      layer = tl.Serial(
          tl.Branch(tl.Embedding(11, 12), tl.PaddingMask()),
          sparsity.Favor(
              d_feature=12,
              n_heads=3,
              n_random_features=128,
              use_approximate_softmax=True,
          ),
          tl.Select([0], n_in=2),
          tl.WeightedCategoryCrossEntropy(),
      )
      x = np.ones((3, 5), dtype=np.int32)
      w = np.ones_like(x).astype(np.float32)
      x_sig = shapes.signature(x)
      w_sig = shapes.signature(w)
      layer_partial.init(x_sig)
      y = layer_partial(x)
      self.assertEqual(y.shape, (3, 5, 12))
      layer.init((x_sig, x_sig, w_sig))
      y = layer((x, x, w))
      self.assertEqual(y.shape, ())
      state = layer.state
      rng = fastmath.random.get_prng(0)
      fwd = lambda weights, inp: layer.pure_fn(
          inp, weights, state, rng=rng
      )[0]
      g = fastmath.grad(fwd)(layer.weights, (x, x, w))
      self.assertEqual(g[0][1][0].shape, (11, 12))
  
imports: import functools
  from absl.testing import parameterized
  import jax
  import numpy
  from tensorflow import test
  from trax import fastmath
  from trax import shapes
  import trax.layers
  from trax.layers import test_utils
  from trax.layers.research import sparsity
label_desc: Use image node_id 2 for calling the FavorTest obj's underlying member method code with example usage: obj.test_call_and_grad_approximate_softmax() without return types