code: def test_terraformer_one_step(self):
      vocab_size = 32
      max_len = 256
      pos_axial = 16
      assert pos_axial * pos_axial == max_len
  
      chunk_len = 32
  
      # Since 2 * chunk_len * n_buckets should be max_len.
      n_buckets = max_len // (2 * chunk_len)
  
      lsh_self_attention = functools.partial(
          self._lsh_self_attention_fn(),
          chunk_len=chunk_len,
          n_buckets=n_buckets,
      )
  
      timebin_self_attention = self._timebin_self_attention_fn()
  
      model = terraformer.ConfigurableTerraformer(
          vocab_size,
          d_model=32,
          d_ff=64,
          d_attention_key=64,
          d_attention_value=64,
          n_encoder_layers=2,
          n_decoder_layers=2,
          n_heads=2,
          dropout=0.05,
          max_len=max_len,
          encoder_attention_type=lsh_self_attention,
          encoder_decoder_attention_type=[
              timebin_self_attention,
              lsh_self_attention,
          ],
          pos_axial_shape=(pos_axial, pos_axial),
          pos_d_axial_embs=(64, 192),
          ff_activation=tl.Relu,
          ff_use_sru=0,
          ff_chunk_size=64,
          ff_sparsity=8,
          mode="train",
      )
  
      x = [
          np.ones((1, max_len)).astype(np.int32),
          np.ones((1, max_len)).astype(np.int32),
      ]
      weights, state = model.init(shapes.signature(x))
  
      @fastmath.jit
      def mock_training_step(x, weights, state, rng):
          def compute_mock_loss(weights):
              logits_and_dec_toks, new_state = model.pure_fn(
                  x, weights, state, rng
              )
              # This returns [logits, decoder tokens]
              logits = logits_and_dec_toks[0]
              loss = fastmath.numpy.mean(logits[..., 0])
              return loss, (new_state, logits)
  
          gradients, (new_state, logits) = fastmath.grad(
              compute_mock_loss, has_aux=True
          )(weights)
          new_weights = fastmath.nested_map_multiarg(
              lambda w, g: w - 1e-4 * g, weights, gradients
          )
          return new_weights, new_state, logits
  
      weights, state, logits = mock_training_step(
          x, weights, state, fastmath.random.get_prng(0)
      )
  
      self.assertEqual(logits.shape, (1, max_len, vocab_size))
  
imports: import functools
  from absl.testing import absltest
  from absl.testing import parameterized
  import gin
  import numpy
  from trax import fastmath
  from trax import layers
  from trax import shapes
  from trax.layers import test_utils
  from trax.models.research import terraformer
label_desc: Use image node_id 8 for calling the TerraformerTest obj's underlying member method code with example usage: obj.test_terraformer_one_step() and returns: new_weights,  new_state,  logits, loss,  new_state,  logits