code: def test_flat_grads(self):
      with contextlib.redirect_stdout(StringIO()):
          with tempfile.TemporaryDirectory(
              "test_flat_grads"
          ) as data_dir:
              # Use just a bit of data and tiny model to keep this test runtime reasonable
              create_dummy_data(data_dir, num_examples=10, maxlen=5)
              preprocess_translation_data(data_dir)
              with self.assertRaises(RuntimeError):
                  # adafactor isn't compatible with flat grads, which
                  # are used by default with --fp16
                  train_translation_model(
                      data_dir,
                      "lstm",
                      [
                          "--required-batch-size-multiple",
                          "1",
                          "--encoder-layers",
                          "1",
                          "--encoder-hidden-size",
                          "32",
                          "--decoder-layers",
                          "1",
                          "--optimizer",
                          "adafactor",
                          "--fp16",
                      ],
                  )
              # but it should pass once we set --fp16-no-flatten-grads
              train_translation_model(
                  data_dir,
                  "lstm",
                  [
                      "--required-batch-size-multiple",
                      "1",
                      "--encoder-layers",
                      "1",
                      "--encoder-hidden-size",
                      "32",
                      "--decoder-layers",
                      "1",
                      "--optimizer",
                      "adafactor",
                      "--fp16",
                      "--fp16-no-flatten-grads",
                  ],
              )
  
imports: import contextlib
  import json
  import logging
  import os
  import tempfile
  import unittest
  from io import StringIO
  import torch
  from fairseq import options
  from fairseq_cli import train
  from tests.utils import create_dummy_data, generate_main, preprocess_lm_data, preprocess_translation_data, train_language_model, train_translation_model
label_desc: Use image node_id 3 for calling the TestOptimizersGPU obj's underlying member method code with example usage: obj.test_flat_grads() without return types