code: def main(
      ckpt_dir: str,
      tokenizer_path: str,
      temperature: float = 0.6,
      top_p: float = 0.9,
      max_seq_len: int = 128,
      max_gen_len: int = 64,
      max_batch_size: int = 4,
  ):
      """
      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.
      """
      generator = Llama.build(
          ckpt_dir=ckpt_dir,
          tokenizer_path=tokenizer_path,
          max_seq_len=max_seq_len,
          max_batch_size=max_batch_size,
      )
  
      prompts: List[str] = [
          # For these prompts, the expected answer is the natural continuation of the prompt
          "I believe the meaning of life is",
          "Simply put, the theory of relativity states that ",
          """A brief message congratulating the team on the launch:
  
          Hi everyone,
          
          I just """,
          # Few shot prompt (providing a few examples before asking model to complete more);
          """Translate English to French:
          
          sea otter => loutre de mer
          peppermint => menthe poivrée
          plush girafe => girafe peluche
          cheese =>""",
      ]
      results = generator.text_completion(
          prompts,
          max_gen_len=max_gen_len,
          temperature=temperature,
          top_p=top_p,
      )
      for prompt, result in zip(prompts, results):
          print(prompt)
          print(f"> {result['generation']}")
          print("\n==================================\n")
  
imports: import fire
  from llama import Llama
  from typing import List
label_desc: Use image node_id 1 for calling a global function with example usage: main(ckpt_dir, tokenizer_path, temperature, top_p, max_seq_len, max_gen_len, max_batch_size) without return types