code: def make_param_dict(
      inputs: Dict[str, BaseType],
      outputs: Dict[str, BaseType],
      component_uid: str,
  ) -> Tuple[Dict[str, Parameter], Dict[str, Parameter]]:
      """Convert exposed input/outputs parameters / dtypes to parameter objects.
  
      Returns
      -------
      Tuple[Dict[str, Parameter], Dict[str, Parameter]]
          Element[0] == Input parameter dict
          Element[1] == Output parameter dict.
  
      """
      flashserve_inp_params, flashserve_out_params = {}, {}
      for inp_key, inp_dtype in inputs.items():
          flashserve_inp_params[inp_key] = Parameter(
              name=inp_key,
              datatype=inp_dtype,
              component_uid=component_uid,
              position="inputs",
          )
  
      for out_key, out_dtype in outputs.items():
          flashserve_out_params[out_key] = Parameter(
              name=out_key,
              datatype=out_dtype,
              component_uid=component_uid,
              position="outputs",
          )
      return flashserve_inp_params, flashserve_out_params
  
imports: import dataclasses
  from dataclasses import dataclass, field, make_dataclass
  from pathlib import Path
  from typing import Dict, Iterator, List, NamedTuple, Optional, Tuple, Type, TypeVar, Union
  import pytorch_lightning
  import torch
  from flash.core.serve.types.base import BaseType
  from flash.core.serve.utils import download_file
  from flash.core.utilities.imports import _PYDANTIC_AVAILABLE, requires
label_desc: Use image node_id 2 for calling a global function with example usage: make_param_dict(inputs, outputs, component_uid) and returns: flashserve_inp_params,  flashserve_out_params