user: Generate python code based on based on the evaluation paper "Co-Writing Screenplays and Theatre Scripts with Language Models", using huggingface transformer library, any available large language model pretrained checkpoint, and python. The functions should be divided into 3 hierarchical layers of abstraction. 

The highest layer is user input, a single sentence describing the central dramatic conflict. 
The middle layer generates character descriptions, a plot outline (a sequence of high-level scene descriptions together with corresponding locations), and location descriptions. 
The bottom layer is generated character dialogue for the text of the script.

assistant: Here's an example implementation in Python using the Hugging Face Transformers library and one of the available large language models, such as GPT-2, as the generator:

```python
import torch
import transformers

# Load the pre-trained language model checkpoint
model = transformers.GPT2LMHeadModel.from_pretrained("gpt2")
model.eval()

def generate_script(conflict: str):
    """
    Generate a script based on the input conflict.
    :param conflict: a string describing the central dramatic conflict.
    :return: a script containing character descriptions, plot outline, location descriptions, and character dialogue.
    """
    script = ""
    
    # High-level: user input
    script += conflict + "\n\n"
    
    # Middle-level: generate character descriptions, plot outline, and location descriptions
    character_descriptions, plot_outline, location_descriptions = generate_middle_layer(conflict)
    script += "Character Descriptions:\n" + character_descriptions + "\n\n"
    script += "Plot Outline:\n" + plot_outline + "\n\n"
    script += "Location Descriptions:\n" + location_descriptions + "\n\n"
    
    # Low-level: generate character dialogue
    dialogue = generate_dialogue(conflict)
    script += "Dialogue:\n" + dialogue
    
    return script

def generate_middle_layer(conflict: str):
    """
    Generate character descriptions, plot outline, and location descriptions.
    :param conflict: a string describing the central dramatic conflict.
    :return: character descriptions, plot outline, and location descriptions as strings.
    """
    character_descriptions = generate_text(conflict, "Character Descriptions: ")
    plot_outline = generate_text(conflict, "Plot Outline: ")
    location_descriptions = generate_text(conflict, "Location Descriptions: ")
    
    return character_descriptions, plot_outline, location_descriptions

def generate_dialogue(conflict: str):
    """
    Generate character dialogue.
    :param conflict: a string describing the central dramatic conflict.
    :return: character dialogue as a string.
    """
    return generate_text(conflict, "Dialogue: ")

def generate_text(prompt: str, prefix: str):
    """
    Generate text using the language model.
    :param prompt: the input prompt.
    :param prefix: the prefix to be added to the prompt.
    :return: generated text as a string.
    """
    input_ids = torch.tensor(tokenizer.encode(prefix + prompt, add_special_tokens=False)).unsqueeze(0)  # add special tokens for the model
    with torch.no_grad():
        outputs = model(input_ids, max_length=1024, num_beams=1, early_stopping=True)
    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    return generated_text
```