code: def test_dialogue_data_processor_get_relevant_idxs():
      dataset_split = "train"
      dev_proportion = 10
      n_samples = 1000
      idxs = DialogueDataProcessor.get_relevant_idxs(
          dataset_split, n_samples, dev_proportion
      )
  
      assert len(idxs) == 900
      assert idxs != list(range(900))
  
      dataset_split = "dev"
      dev_proportion = 40
      n_samples = 1000
      idxs = DialogueDataProcessor.get_relevant_idxs(
          dataset_split, n_samples, dev_proportion
      )
  
      assert len(idxs) == 400
      assert idxs != list(range(400))
  
      dataset_split = "test"
      dev_proportion = 40
      n_samples = 1000
      idxs = DialogueDataProcessor.get_relevant_idxs(
          dataset_split, n_samples, dev_proportion
      )
  
      assert len(idxs) == 1000
      assert idxs == list(range(1000))
  
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 7 for calling a global function with example usage: test_dialogue_data_processor_get_relevant_idxs() without return types