code: def test_dialogue_metric_split_label_and_slots():
      fields = [
          "reserve_restaurant\nslots: time_of_day(7pm), number_of_people(3)",
          "time_of_day(7pm)",
      ]
      (
          labels,
          slots_list,
      ) = DialogueClassificationMetrics.split_label_and_slots(
          fields, with_slots=True
      )
      assert labels == ["reserve_restaurant", "none"]
      assert slots_list == [
          ["time_of_day(7pm)", "number_of_people(3)"],
          ["time_of_day(7pm)"],
      ]
  
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 2 for calling a global function with example usage: test_dialogue_metric_split_label_and_slots() without return types