code: def test_optimizers(self):
      for opt_class in OPTIMIZERS.values():
          with tf.Graph().as_default():
              loss_var = tf.compat.v1.get_variable("loss", shape=1)
              loss = tf.abs(loss_var)
              lr = 0.1
              opt = opt_class(
                  lr, weight_decay=1e-10, decay_var_list=[loss_var]
              )
              if isinstance(opt, tf.keras.optimizers.Optimizer):
                  train_op = opt.minimize(lambda: loss, [loss_var])
              else:
                  train_op = opt.minimize(loss)
  
              sess = tf.compat.v1.Session()
              sess.run(tf.compat.v1.global_variables_initializer())
              original_loss = sess.run(loss)
              for i in range(10):
                  sess.run(train_op)
          self.assertLess(sess.run(loss), original_loss)
  
imports: import unittest
  from collections import Counter
  import pytest
  import numpy
  import tensorflow
  import unicodedata
  from finetune.encoding.sequence_encoder import finetune_to_indico_sequence
  from finetune.optimizers.gradient_accumulation import get_grad_accumulation_optimizer
  from finetune.util.imbalance import compute_class_weights
  from finetune.util.optimize_loss import OPTIMIZERS
  from finetune.util.timing import ProgressBar
  from finetune.errors import FinetuneError
  from finetune import Classifier, SequenceLabeler
  from finetune.base_models.gpt.encoder import GPTEncoder
  from finetune.base_models.gpt2.encoder import GPT2Encoder
  from finetune.base_models.bert.roberta_encoder import RoBERTaEncoderV2, RoBERTaEncoder, RoBERTaEncoderSlow
  from finetune.base_models.bert.encoder import BERTEncoderMultuilingal, BERTEncoder
  from finetune.base_models.oscar.encoder import GPCEncoder
  from finetune.scheduler import Scheduler
label_desc: Use image node_id 1 for calling the TestOptimizers obj's underlying member method code with example usage: obj.test_optimizers() without return types