code: def create_eval_results_json(
      prompt_parameters,
      model_input,
      model_output_parameters,
      model_output,
  ):
      columns = [param.key for param in prompt_parameters] + [
          "prompt",
          "output",
      ]
      data = [param.value for param in prompt_parameters] + [
          model_input,
          model_output,
      ]
  
      updated_columns = columns + [
          param.key for param in model_output_parameters
      ]
      updated_data = data + [
          param.value for param in model_output_parameters
      ]
  
      eval_results = {
          "columns": updated_columns,
          "data": [updated_data],
      }
  
      return json.dumps(eval_results)
  
file_path: mlflow/mlflow/utils/promptlab_utils.py
imports: import json
  import os
  import tempfile
  import time
  from datetime import datetime
  from typing import List
  from mlflow.entities.param import Param
  from mlflow.entities.run_status import RunStatus
  from mlflow.entities.run_tag import RunTag
  from mlflow.utils.file_utils import make_containing_dirs, write_to
  from mlflow.utils.mlflow_tags import MLFLOW_LOGGED_ARTIFACTS, MLFLOW_RUN_SOURCE_TYPE
  from mlflow.version import VERSION
label_desc: Use image node_id 1 for calling a global function with example usage: create_eval_results_json(prompt_parameters, model_input, model_output_parameters, model_output) and returns: json