code: def build_and_evalute_model_with_prompt(prompt_template):
      mlflow.start_run()
      mlflow.log_param("prompt_template", prompt_template)
      # Create a news summarization model using prompt engineering with LangChain. Log the model
      # to MLflow Tracking
      llm = OpenAI(temperature=0.9)
      prompt = PromptTemplate(
          input_variables=["article"], template=prompt_template
      )
      chain = LLMChain(llm=llm, prompt=prompt)
      logged_model = mlflow.langchain.log_model(
          chain, artifact_path="model"
      )
  
      # Evaluate the model on a small sample dataset
      sample_data = pd.read_csv("summarization_example_data.csv")
      mlflow.evaluate(
          model=logged_model.model_uri,
          model_type="text-summarization",
          data=sample_data,
          targets="highlights",
      )
      mlflow.end_run()
  
imports: import os
  import pandas
  from langchain.chains import LLMChain
  from langchain.llms import OpenAI
  from langchain.prompts import PromptTemplate
  import mlflow
label_desc: Use image node_id 1 for calling a global function with example usage: build_and_evalute_model_with_prompt(prompt_template) without return types