code: def main():
      """
      Entry point of the program for generating text using a pretrained model.
      Args:
          ckpt_dir (str): The directory containing checkpoint files for the pretrained model.
          tokenizer_path (str): The path to the tokenizer model used for text encoding/decoding.
          temperature (float, optional): The temperature value for controlling randomness in generation.
              Defaults to 0.6.
          top_p (float, optional): The top-p sampling parameter for controlling diversity in generation.
              Defaults to 0.9.
          max_seq_len (int, optional): The maximum sequence length for input prompts. Defaults to 128.
          max_gen_len (int, optional): The maximum length of generated sequences. Defaults to 64.
          max_batch_size (int, optional): The maximum batch size for generating sequences. Defaults to 4.
      """
  
      prompts: List[Tuple[List[str], AgentType]] = [
          (["<Sample user prompt>"], AgentType.USER),
          (
              ["<Sample user prompt>", "<Sample agent response>"],
              AgentType.AGENT,
          ),
          (
              [
                  "<Sample user prompt>",
                  "<Sample agent response>",
                  "<Sample user reply>",
                  "<Sample agent response>",
              ],
              AgentType.AGENT,
          ),
      ]
  
      model_id = "meta-llama/LlamaGuard-7b"
  
      tokenizer = AutoTokenizer.from_pretrained(model_id)
      model = AutoModelForCausalLM.from_pretrained(
          model_id, load_in_8bit=True, device_map="auto"
      )
  
      for prompt in prompts:
          formatted_prompt = build_prompt(
              prompt[1],
              LLAMA_GUARD_CATEGORY,
              create_conversation(prompt[0]),
          )
  
          input = tokenizer([formatted_prompt], return_tensors="pt").to(
              "cuda"
          )
          prompt_len = input["input_ids"].shape[-1]
          output = model.generate(
              **input, max_new_tokens=100, pad_token_id=0
          )
          results = tokenizer.decode(
              output[0][prompt_len:], skip_special_tokens=True
          )
  
          print(prompt[0])
          print(f"> {results}")
          print("\n==================================\n")
  
file_path: llama-recipes/examples/llama_guard/inference.py
imports: import fire
  from transformers import AutoTokenizer, AutoModelForCausalLM
  from llama_recipes.inference.prompt_format_utils import build_prompt, create_conversation, LLAMA_GUARD_CATEGORY
  from typing import List, Tuple
  from enum import Enum
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types