text: # AUTOGENERATED BY NBDEV! DO NOT EDIT!
  
  __all__ = ["index", "modules", "custom_doc_links", "git_url"]
  
  index = {"extract_dataset": "00_extract.ipynb",
           "save_to_csv": "00_extract.ipynb",
           "extract_dataset_threaded": "00_extract.ipynb",
           "clean_genre": "01_eda.ipynb",
           "freq_words": "01_eda.ipynb",
           "freq_words_sw": "01_eda.ipynb",
           "req_image": "02_clean.ipynb",
           "extract_images_threaded": "02_clean.ipynb",
           "split_datetime": "02_clean.ipynb",
           "remove_sw": "02_clean.ipynb",
           "drop_missing": "02_clean.ipynb",
           "drop_col": "02_clean.ipynb",
           "create_splits": "02_clean.ipynb",
           "MovieDataset": "03_dataset.ipynb",
           "Tokenize": "03_dataset.ipynb",
           "RandomResizeCrop": "03_dataset.ipynb",
           "ToTensor": "03_dataset.ipynb",
           "NormalizeStandardize": "03_dataset.ipynb",
           "Compose": "03_dataset.ipynb",
           "freeze_all_but_layer": "04_model.ipynb",
           "cnn_encoder": "04_model.ipynb",
           "text_encoder": "04_model.ipynb",
           "meta_encoder": "04_model.ipynb",
           "decoder": "04_model.ipynb",
           "TaglinePredictorModel": "04_model.ipynb",
           "TagLineLoss": "05_train.ipynb",
           "splitter_func": "05_train.ipynb",
           "load_model": "05_train.ipynb",
           "predict_one_batch": "05_train.ipynb"}
  
  modules = ["extract.py",
             "eda.py",
             "clean.py",
             "dataset.py",
             "model.py",
             "train.py"]
  
  doc_url = "https://Benjamintdk.github.io/DSAI_proj/"
  
  git_url = "https://github.com/Benjamintdk/DSAI_proj/tree/main/"
  
  def custom_doc_links(name): return None
  