code: def init(model: PyFuncModel):
      """
      Initialize the server. Loads pyfunc model from the path.
      """
      app = flask.Flask(__name__)
      input_schema = model.metadata.get_input_schema()
  
      @app.route("/ping", methods=["GET"])
      @app.route("/health", methods=["GET"])
      def ping():
          """
          Determine if the container is working and healthy.
          We declare it healthy if we can load the model successfully.
          """
          health = model is not None
          status = 200 if health else 404
          return flask.Response(
              response="\n", status=status, mimetype="application/json"
          )
  
      @app.route("/version", methods=["GET"])
      def version():
          """
          Returns the current mlflow version.
          """
          return flask.Response(
              response=VERSION, status=200, mimetype="application/json"
          )
  
      @app.route("/invocations", methods=["POST"])
      @catch_mlflow_exception
      def transformation():
          """
          Do an inference on a single batch of data. In this sample server,
          we take data as CSV or json, convert it to a Pandas DataFrame or Numpy,
          generate predictions and convert them back to json.
          """
  
          # Content-Type can include other attributes like CHARSET
          # Content-type RFC: https://datatracker.ietf.org/doc/html/rfc2045#section-5.1
          # TODO: Suport ";" in quoted parameter values
          data = flask.request.data.decode("utf-8")
          content_type = flask.request.content_type
          result = invocations(data, content_type, model, input_schema)
  
          return flask.Response(
              response=result.response,
              status=result.status,
              mimetype=result.mimetype,
          )
  
      return app
  
imports: import inspect
  import json
  import logging
  import os
  import shlex
  import sys
  import traceback
  from typing import Dict, NamedTuple, Optional, Tuple
  import flask
  from mlflow.environment_variables import MLFLOW_SCORING_SERVER_REQUEST_TIMEOUT
  from mlflow.exceptions import MlflowException
  from mlflow.pyfunc.model import _log_warning_if_params_not_in_predict_signature
  from mlflow.types import ParamSchema, Schema
  from mlflow.utils import reraise
  from mlflow.utils.annotations import deprecated
  from mlflow.utils.file_utils import path_to_local_file_uri
  from mlflow.utils.os import is_windows
  from mlflow.utils.proto_json_utils import MlflowInvalidInputException, NumpyEncoder, _get_jsonable_obj, dataframe_from_parsed_json, parse_tf_serving_input
  from mlflow.version import VERSION
  from io import StringIO
  from mlflow.protos.databricks_pb2 import BAD_REQUEST, INVALID_PARAMETER_VALUE
  from mlflow.server.handlers import catch_mlflow_exception
label_desc: Use image node_id 11 for calling a global function with example usage: init(model) and returns: app, flask, flask, flask