code: def from_spaces_interface(
      model_name: str,
      config: dict,
      alias: str | None,
      hf_token: str | None,
      iframe_url: str,
      **kwargs,
  ) -> Interface:
      config = streamline_spaces_interface(config)
      api_url = f"{iframe_url}/api/predict/"
      headers = {"Content-Type": "application/json"}
      if hf_token is not None:
          headers["Authorization"] = f"Bearer {hf_token}"
  
      # The function should call the API with preprocessed data
      def fn(*data):
          data = json.dumps({"data": data})
          response = httpx.post(api_url, headers=headers, data=data)  # type: ignore
          result = json.loads(response.content.decode("utf-8"))
          if "error" in result and "429" in result["error"]:
              raise TooManyRequestsError(
                  "Too many requests to the Hugging Face API"
              )
          try:
              output = result["data"]
          except KeyError as ke:
              raise KeyError(
                  f"Could not find 'data' key in response from external Space. Response received: {result}"
              ) from ke
          if (
              len(config["outputs"]) == 1
          ):  # if the fn is supposed to return a single value, pop it
              output = output[0]
          if len(config["outputs"]) == 1 and isinstance(
              output, list
          ):  # Needed to support Output.Image() returning bounding boxes as well (TODO: handle different versions of gradio since they have slightly different APIs)
              output = output[0]
          return output
  
      fn.__name__ = alias if (alias is not None) else model_name
      config["fn"] = fn
  
      kwargs = dict(config, **kwargs)
      kwargs["_api_mode"] = True
      interface = gradio.Interface(**kwargs)
      return interface
  
imports: from __future__ import annotations
  import json
  import os
  import re
  import tempfile
  import warnings
  from pathlib import Path
  from typing import TYPE_CHECKING, Callable
  import httpx
  from gradio_client import Client
  from gradio_client import utils
  from gradio_client.client import Endpoint
  from gradio_client.documentation import document, set_documentation_group
  from packaging import version
  import gradio
  from gradio import components, utils
  from gradio.context import Context
  from gradio.exceptions import Error, GradioVersionIncompatibleError, ModelNotFoundError, TooManyRequestsError
  from gradio.external_utils import cols_to_rows, encode_to_base64, get_tabular_examples, postprocess_label, rows_to_cols, streamline_spaces_interface
  from gradio.processing_utils import extract_base64_data, save_base64_to_cache, to_binary
label_desc: Use image node_id 8 for calling a global function with example usage: from_spaces_interface(model_name, config, alias, hf_token, iframe_url) and returns: interface, output