class_docstr: The MLflow integration engine needs to have a working connection to MLFlow. For this:
      - All models to use should be previously served
      - An MLflow server should be running, to access its model registry
  
  Example:
      1. Run `mlflow server -p 5001 --backend-store-uri sqlite:////path/to/mlflow.db --default-artifact-root ./artifacts --host 0.0.0.0`
      2. Run `mlflow models serve --model-uri ./model_path`
      3. Run MindsDB
  
  Note: above, `artifacts` is a folder to store artifacts for new experiments that do not specify an artifact store.
docstr: try post without data, check status code not in (not_found, method_not_allowed) 
code: def _check_model_url(url):
      """try post without data, check status code not in (not_found, method_not_allowed)"""
      try:
          resp = requests.post(url)
          if resp.status_code in (404, 405):
              raise Exception(
                  f"Model url is incorrect, status_code: {resp.status_code}"
              )
      except requests.RequestException as e:
          raise Exception(f"Model url is incorrect: {str(e)}")
  
file_path: mindsdb/mindsdb/integrations/handlers/mlflow_handler/mlflow_handler.py
imports: import requests
  from datetime import datetime
  from typing import Dict, Optional
  import pandas
  from mlflow.tracking import MlflowClient
  from mindsdb.integrations.libs.base import BaseMLEngine
label_desc: Use image node_id 4 for calling the MLflowHandler obj's underlying member method code with example usage: obj._check_model_url(url) without return types