code: def test_save_single(self):
      model = SequenceLabeler()
      model.fit(
          ["test text"] * 20,
          [[{"start": 0, "end": 4, "label": "test"}]] * 20,
      )
      preds_a = model.predict(["test text"])
      SequenceLabeler.save_multiple("multiple_models.jl", {"a": model})
      loaded_model = SequenceLabeler.load("multiple_models.jl", key="a")
      preds_b = loaded_model.predict(["test text"])
      shed = Scheduler()
      preds_c = shed.predict(
          "multiple_models.jl", ["test text"], key="a"
      )
      assert preds_a[
          0
      ]  # If this is empty it's not a good test that nothing changed.
      self.preds_equal(preds_a, preds_b)
      self.preds_equal(preds_b, preds_c)
  
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 2 for calling the TestSaveMultiple obj's underlying member method code with example usage: obj.test_save_single() without return types