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.
code: def describe(self, key: Optional[str] = None) -> pd.DataFrame:
      if key == "info":
          args = self.model_storage.json_get("args")
          connection = MlflowClient(
              args["mlflow_server_url"], args["self.mlflow_server_path"]
          )
          models = {
              model.name: model
              for model in connection.search_registered_models()
          }
          model = models[key]
          latest_version = model.latest_versions[-1]
          description = {
              "NAME": [model.name],
              "USER_DESCRIPTION": [model.description],
              "LAST_STATUS": [latest_version.status],
              "CREATED_AT": [
                  datetime.fromtimestamp(
                      model.creation_timestamp // 1000
                  ).strftime("%m/%d/%Y, %H:%M:%S")
              ],
              "LAST_UPDATED": [
                  datetime.fromtimestamp(
                      model.last_updated_timestamp // 1000
                  ).strftime("%m/%d/%Y, %H:%M:%S")
              ],
              "TAGS": [model.tags],
              "LAST_RUN_ID": [latest_version.run_id],
              "LAST_SOURCE_PATH": [latest_version.source],
              "LAST_USER_ID": [latest_version.user_id],
              "LAST_VERSION": [latest_version.version],
          }
          return pd.DataFrame.from_dict(description)
      else:
          tables = ["info"]
          return pd.DataFrame(tables, columns=["tables"])
  
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 3 for calling the MLflowHandler obj's underlying member method code with example usage: obj.describe(key) and returns: pd, pd