code: def test_loss_layer_timing(self):
      all_settings = [
          # The first run is sometimes slower, less reliable.
          {
              "output": 32000,
              "input": 2048,
              "prob": None,
              "type": None,
              "sparsity": 0,
              "lowrank": 0,
              "use_bias": False,
          },
          {
              "output": 32000,
              "input": 2048,
              "prob": None,
              "type": None,
              "sparsity": 0,
              "lowrank": 0,
              "use_bias": False,
          },
          {
              "output": 32000,
              "input": 2048,
              "prob": None,
              "type": "einsum",
              "sparsity": 0,
              "lowrank": 0,
              "use_bias": False,
          },
          {
              "output": 32000,
              "input": 2048,
              "prob": None,
              "type": "mult",
              "sparsity": 2,
              "lowrank": 0,
              "use_bias": False,
          },
          {
              "output": 32000,
              "input": 2048,
              "prob": None,
              "type": None,
              "sparsity": 0,
              "lowrank": 0,
              "use_bias": True,
          },
          {
              "output": 32000,
              "input": 2048,
              "prob": None,
              "type": "einsum",
              "sparsity": 0,
              "lowrank": 0,
              "use_bias": True,
          },
          {
              "output": 32000,
              "input": 2048,
              "prob": None,
              "type": "mult",
              "sparsity": 2,
              "lowrank": 0,
              "use_bias": True,
          },
      ]
  
      messages = []
      for settings in all_settings:
          pred_model = tl.SparseDenseWithOptions(
              n_units=settings["output"],
              d_input=settings["input"],
              sparsity_type=settings["type"],
              sparsity=settings["sparsity"],
              d_lowrank=settings["lowrank"],
              prob_sparse=settings["prob"],
              use_bias=settings["use_bias"],
              mode="predict",
          )
          pred_model = tl.Accelerate(pred_model)
  
          shape1l = shapes.ShapeDtype((1, settings["input"]))
          pred_model.init(input_signature=shape1l)
          inputs = np.ones((1, settings["input"]))
  
          total_time = 0.0
          for counter in range(-50, 100):
              start_time = time.time()
              y = pred_model(inputs)
              self.assertEqual(y.shape, (1, settings["output"]))
              elapsed_time = time.time() - start_time
              if counter >= 0:
                  total_time += elapsed_time
  
          message = (
              "\n\nParams: %d Settings: %s\nTime for 100 tokens: %.4f s\n\n\n"
              % (_size_of_model(pred_model), settings, total_time)
          )
          messages.append(message)
          print(message)
  
      print("Final results (recap):")
      for message in messages:
          print(message)
  
imports: import copy
  import functools
  import gc
  import os
  import time
  from jax import config
  import numpy
  import psutil
  from tensorflow.compat.v2 import test
  from trax import fastmath
  from trax import layers
  from trax import models
  from trax import shapes
  from trax.supervised import decoding
label_desc: Use image node_id 3 for calling the DecodingTimingTest obj's underlying member method code with example usage: obj.test_loss_layer_timing() without return types