code: def get_cmd(
      model_uri: str,
      port: Optional[int] = None,
      host: Optional[str] = None,
      timeout: Optional[int] = None,
      nworkers: Optional[int] = None,
      model_name: Optional[str] = None,
      model_version: Optional[str] = None,
  ) -> Tuple[str, Dict[str, str]]:
      cmd = f"mlserver start {model_uri}"
  
      cmd_env = os.environ.copy()
  
      if port:
          cmd_env["MLSERVER_HTTP_PORT"] = str(port)
  
      if host:
          cmd_env["MLSERVER_HOST"] = host
  
      if timeout:
          _logger.warning("Timeout is not yet supported in MLServer.")
  
      if nworkers:
          cmd_env["MLSERVER_PARALLEL_WORKERS"] = str(nworkers)
  
      # give precedence to user env var input
      cmd_env["MLSERVER_MODEL_NAME"] = (
          cmd_env.get("MLSERVER_MODEL_NAME")
          or model_name
          or MLServerDefaultModelName
      )
      if model_version and not cmd_env.get("MLSERVER_MODEL_VERSION"):
          cmd_env["MLSERVER_MODEL_VERSION"] = model_version
  
      cmd_env["MLSERVER_MODEL_IMPLEMENTATION"] = MLServerMLflowRuntime
      cmd_env["MLSERVER_MODEL_URI"] = model_uri
  
      return cmd, cmd_env
  
imports: import logging
  import os
  from typing import Dict, Optional, Tuple
label_desc: Use image node_id 1 for calling a global function with example usage: get_cmd(model_uri, port, host, timeout, nworkers, model_name, model_version) and returns: cmd,  cmd_env