code: def _validate_model_params(task, model, params):
      if not params:
          return
  
      if any(key in model for key in params):
          raise mlflow.MlflowException.invalid_parameter_value(
              f"Providing any of {list(model.keys())} as parameters in the signature is not "
              "allowed because they were indicated as part of the OpenAI model. Either remove "
              "the argument when logging the model or remove the parameter from the signature.",
          )
      if "batch_size" in params and task == "chat.completions":
          raise mlflow.MlflowException.invalid_parameter_value(
              "Parameter `batch_size` is not supported for task `chat.completions`"
          )
  
imports: import json
  import os
  import time
  from contextlib import contextmanager
  from enum import Enum
  from typing import NamedTuple, Optional
  from unittest import mock
  import requests
  import mlflow
label_desc: Use image node_id 11 for calling a global function with example usage: _validate_model_params(task, model, params) without return types