code: def test_dialogue_gpt_classification_dataset_linearize_slots():
      slots = []
      linearized_slots = "None"
      assert (
          linearized_slots
          == DialogueGPTClassificationDataset.linearize_slots(slots)
      )
  
      slots = {"time": "7pm", "place": "field"}
      linearized_slots = "time(7pm), place(field)"
      assert (
          linearized_slots
          == DialogueGPTClassificationDataset.linearize_slots(slots)
      )
  
      slots = {"time": ["7pm", "1900"], "place": "field"}
      linearized_slots = "time(7pm), place(field)"
      assert (
          linearized_slots
          == DialogueGPTClassificationDataset.linearize_slots(slots)
      )
  
imports: import pytest
  import torch
  from nemo.collections.nlp.data.dialogue.data_processor.assistant_data_processor import DialogueAssistantDataProcessor
  from nemo.collections.nlp.data.dialogue.data_processor.data_processor import DialogueDataProcessor
  from nemo.collections.nlp.data.dialogue.data_processor.sgd_data_processor import DialogueSGDDataProcessor
  from nemo.collections.nlp.data.dialogue.dataset.dialogue_gpt_classification_dataset import DialogueGPTClassificationDataset
  from nemo.collections.nlp.data.dialogue.dataset.dialogue_s2s_generation_dataset import DialogueS2SGenerationDataset
  from nemo.collections.nlp.data.dialogue.dataset.dialogue_sgd_bert_dataset import DialogueSGDBERTDataset
  from nemo.collections.nlp.metrics.dialogue_metrics import DialogueClassificationMetrics, DialogueGenerationMetrics
  from nemo.collections.nlp.models.dialogue.dialogue_nearest_neighbour_model import DialogueNearestNeighbourModel
label_desc: Use image node_id 9 for calling a global function with example usage: test_dialogue_gpt_classification_dataset_linearize_slots() without return types