code: def notebooks():
      folder_notebooks = path_notebooks()
  
      # Path for the notebooks
      paths = {
          "template": os.path.join(folder_notebooks, "template.ipynb"),
          "sar_single_node": os.path.join(
              folder_notebooks, "00_quick_start", "sar_movielens.ipynb"
          ),
          "ncf": os.path.join(
              folder_notebooks, "00_quick_start", "ncf_movielens.ipynb"
          ),
          "als_pyspark": os.path.join(
              folder_notebooks, "00_quick_start", "als_movielens.ipynb"
          ),
          "fastai": os.path.join(
              folder_notebooks,
              "00_quick_start",
              "fastai_movielens.ipynb",
          ),
          "xdeepfm_quickstart": os.path.join(
              folder_notebooks, "00_quick_start", "xdeepfm_criteo.ipynb"
          ),
          "dkn_quickstart": os.path.join(
              folder_notebooks, "00_quick_start", "dkn_MIND.ipynb"
          ),
          "lightgbm_quickstart": os.path.join(
              folder_notebooks,
              "00_quick_start",
              "lightgbm_tinycriteo.ipynb",
          ),
          "wide_deep": os.path.join(
              folder_notebooks,
              "00_quick_start",
              "wide_deep_movielens.ipynb",
          ),
          "slirec_quickstart": os.path.join(
              folder_notebooks,
              "00_quick_start",
              "sequential_recsys_amazondataset.ipynb",
          ),
          "nrms_quickstart": os.path.join(
              folder_notebooks, "00_quick_start", "nrms_MIND.ipynb"
          ),
          "naml_quickstart": os.path.join(
              folder_notebooks, "00_quick_start", "naml_MIND.ipynb"
          ),
          "lstur_quickstart": os.path.join(
              folder_notebooks, "00_quick_start", "lstur_MIND.ipynb"
          ),
          "npa_quickstart": os.path.join(
              folder_notebooks, "00_quick_start", "npa_MIND.ipynb"
          ),
          "rlrmc_quickstart": os.path.join(
              folder_notebooks,
              "00_quick_start",
              "rlrmc_movielens.ipynb",
          ),
          "geoimc_quickstart": os.path.join(
              folder_notebooks,
              "00_quick_start",
              "geoimc_movielens.ipynb",
          ),
          "sasrec_quickstart": os.path.join(
              folder_notebooks, "00_quick_start", "sasrec_amazon.ipynb"
          ),
          "data_split": os.path.join(
              folder_notebooks, "01_prepare_data", "data_split.ipynb"
          ),
          "wikidata_knowledge_graph": os.path.join(
              folder_notebooks,
              "01_prepare_data",
              "wikidata_knowledge_graph.ipynb",
          ),
          "mind_utils": os.path.join(
              folder_notebooks, "01_prepare_data", "mind_utils.ipynb"
          ),
          "als_deep_dive": os.path.join(
              folder_notebooks,
              "02_model_collaborative_filtering",
              "als_deep_dive.ipynb",
          ),
          "surprise_svd_deep_dive": os.path.join(
              folder_notebooks,
              "02_model_collaborative_filtering",
              "surprise_svd_deep_dive.ipynb",
          ),
          "baseline_deep_dive": os.path.join(
              folder_notebooks,
              "02_model_collaborative_filtering",
              "baseline_deep_dive.ipynb",
          ),
          "lightgcn_deep_dive": os.path.join(
              folder_notebooks,
              "02_model_collaborative_filtering",
              "lightgcn_deep_dive.ipynb",
          ),
          "ncf_deep_dive": os.path.join(
              folder_notebooks,
              "02_model_collaborative_filtering",
              "ncf_deep_dive.ipynb",
          ),
          "sar_deep_dive": os.path.join(
              folder_notebooks,
              "02_model_collaborative_filtering",
              "sar_deep_dive.ipynb",
          ),
          "vowpal_wabbit_deep_dive": os.path.join(
              folder_notebooks,
              "02_model_content_based_filtering",
              "vowpal_wabbit_deep_dive.ipynb",
          ),
          "mmlspark_lightgbm_criteo": os.path.join(
              folder_notebooks,
              "02_model_content_based_filtering",
              "mmlspark_lightgbm_criteo.ipynb",
          ),
          "cornac_bpr_deep_dive": os.path.join(
              folder_notebooks,
              "02_model_collaborative_filtering",
              "cornac_bpr_deep_dive.ipynb",
          ),
          "cornac_bivae_deep_dive": os.path.join(
              folder_notebooks,
              "02_model_collaborative_filtering",
              "cornac_bivae_deep_dive.ipynb",
          ),
          "xlearn_fm_deep_dive": os.path.join(
              folder_notebooks,
              "02_model_collaborative_filtering",
              "fm_deep_dive.ipynb",
          ),
          "lightfm_deep_dive": os.path.join(
              folder_notebooks,
              "02_model_collaborative_filtering",
              "lightfm_deep_dive.ipynb",
          ),
          "evaluation": os.path.join(
              folder_notebooks, "03_evaluate", "evaluation.ipynb"
          ),
          "evaluation_diversity": os.path.join(
              folder_notebooks,
              "03_evaluate",
              "als_movielens_diversity_metrics.ipynb",
          ),
          "spark_tuning": os.path.join(
              folder_notebooks,
              "04_model_select_and_optimize",
              "tuning_spark_als.ipynb",
          ),
          "nni_tuning_svd": os.path.join(
              folder_notebooks,
              "04_model_select_and_optimize",
              "nni_surprise_svd.ipynb",
          ),
          "benchmark_movielens": os.path.join(
              folder_notebooks, "06_benchmarks", "movielens.ipynb"
          ),
      }
      return paths
  
imports: import calendar
  import datetime
  import os
  from pathlib import Path
  from tempfile import TemporaryDirectory
  import numpy
  import pandas
  import pytest
  from sklearn.model_selection import train_test_split
  from recommenders.utils.constants import DEFAULT_USER_COL, DEFAULT_ITEM_COL, DEFAULT_RATING_COL, DEFAULT_TIMESTAMP_COL
  from recommenders.datasets.python_splitters import numpy_stratified_split
  from recommenders.datasets.python_splitters import python_chrono_split
  from recommenders.utils.spark_utils import start_or_get_spark
label_desc: Use image node_id 14 for calling a global function with example usage: notebooks() and returns: paths