code: def generate_alpaca():
      # We can convert dataset with the same format("instruction", "input", "output") as Alpaca into a one-round conversation.
      conversation_dataset = []
      dataset = load_dataset("tatsu-lab/alpaca", split="train")
  
      instructions = dataset["instruction"]
      inputs = dataset["input"]
      outputs = dataset["output"]
  
      assert len(instructions) == len(inputs) == len(outputs)
  
      for idx in range(len(instructions)):
          human_utterance = (
              instructions[idx] + "\n\n" + inputs[idx]
              if inputs[idx]
              else instructions[idx]
          )
          human = {"from": "human", "value": human_utterance}
  
          gpt_utterance = outputs[idx]
          gpt = {"from": "gpt", "value": gpt_utterance}
  
          conversation = dict(
              type="instruction",
              language="English",
              dataset="Alpaca",
              conversations=[human, gpt],
          )
          conversation_dataset.append(conversation)
  
      return conversation_dataset
  
imports: import argparse
  import json
  from datasets import load_dataset
label_desc: Use image node_id 1 for calling a global function with example usage: generate_alpaca() and returns: conversation_dataset