code: def _test_fast_inference(
      self, model_cls, x, input_signature, common_kwargs, *test_kwargs
  ):
      ref_model = model_cls(
          use_reference_code=True, mode="eval", **common_kwargs
      )
      weights, state = ref_model.init(input_signature)
  
      ref_out, _ = ref_model.pure_fn(
          x, weights, state, rng=jax.random.PRNGKey(0)
      )
  
      def get_slice(pytree, i):
          def get_slice_for_val(x):
              if isinstance(x, shapes.ShapeDtype):
                  return shapes.ShapeDtype(
                      shape=x.shape[:1] + (1,) + x.shape[2:],
                      dtype=x.dtype,
                  )
              else:
                  return x[:, i : i + 1]
  
          return jax.tree_map(get_slice_for_val, pytree)
  
      seqlen = (
          x[0].shape[1] if isinstance(x, (tuple, list)) else x.shape[1]
      )
  
      for kwargs in test_kwargs:
          test_model = model_cls(
              mode="predict", **common_kwargs, **kwargs
          )
          cur_state = test_model.init(get_slice(input_signature, 0))[1]
          out = []
          for i in range(seqlen):
              cur_out, cur_state = test_model.pure_fn(
                  get_slice(x, i),
                  weights,
                  cur_state,
                  jax.random.PRNGKey(0),
              )
              out.append(cur_out)
          out = jnp.concatenate(out, axis=1)
  
          self.assertAllClose(out, ref_out, rtol=1e-3, atol=1e-3)
  
imports: from absl.testing import parameterized
  import jax
  import numpy
  from tensorflow import test
  from trax import fastmath
  from trax import shapes
  from trax.fastmath import numpy
  from trax.layers.research import efficient_attention
label_desc: Use image node_id 9 for calling the EfficientAttentionTest obj's underlying member method code with example usage: obj._test_fast_inference(model_cls, x, input_signature, common_kwargs) and returns: jax, shapes, x