code: def build_and_evalute_model_with_prompt(system_prompt):
      mlflow.start_run()
      mlflow.log_param("system_prompt", system_prompt)
  
      # Create a question answering model using prompt engineering with OpenAI. Log the model
      # to MLflow Tracking
      logged_model = mlflow.openai.log_model(
          model="gpt-3.5-turbo",
          task=openai.ChatCompletion,
          artifact_path="model",
          messages=[
              {"role": "system", "content": system_prompt},
              {"role": "user", "content": "{question}"},
          ],
      )
  
      # Evaluate the model on some example questions
      questions = pd.DataFrame(
          {
              "question": [
                  "How do you create a run with MLflow?",
                  "How do you log a model with MLflow?",
                  "What is the capital of France?",
              ]
          }
      )
      mlflow.evaluate(
          model=logged_model.model_uri,
          model_type="question-answering",
          data=questions,
      )
      mlflow.end_run()
  
imports: import os
  import openai
  import pandas
  import mlflow
label_desc: Use image node_id 1 for calling a global function with example usage: build_and_evalute_model_with_prompt(system_prompt) without return types