code: def test_init(mock_auto_tokenizer):
      df = pd.DataFrame(
          {
              "col_A": [1, 2, 3],
              "col_B": [4, 5, 6],
          }
      )
      cfg = mock.MagicMock()
      cfg.dataset.prompt_column = "col_A"
      cfg.dataset.answer_column = "col_B"
      cfg.dataset.parent_id_column = "None"
      cfg.dataset.system_column = "None"
  
      cfg.dataset.text_system_start = ""
      cfg.dataset.text_prompt_start = ""
      cfg.dataset.text_answer_separator = ""
  
      dataset = CustomDataset(df, cfg)
  
      assert dataset.df.equals(df)
      assert dataset.mode == "train"
  
file_path: h2o-llmstudio/tests/src/datasets/test_text_causal_language_modeling_ds.py
imports: from unittest import mock
  from unittest.mock import MagicMock, patch
  import numpy
  import pandas
  import pytest
  from llm_studio.app_utils.default_datasets import prepare_default_dataset_causal_language_modeling
  from llm_studio.python_configs.text_causal_language_modeling_config import ConfigNLPCausalLMDataset, ConfigNLPCausalLMTokenizer, ConfigProblemBase
  from llm_studio.src.datasets.text_causal_language_modeling_ds import CustomDataset
label_desc: Use image node_id 5 for calling a global function with example usage: test_init(mock_auto_tokenizer) without return types