code: def initialize_model():
      """
      API Endpoint to instantiate a model
  
      Loads ASR model by its pretrained checkpoint name or upload ASR model that is provided by the user,
      then load that checkpoint into the cache.
  
      Loading of the model into cache is done once per worker. Number of workers should be limited
      so as not to exhaust the GPU memory available on device (if GPU is being used).
      """
      logging.info("Starting ASR service")
      if torch.cuda.is_available():
          logging.info("CUDA is available. Running on GPU")
      else:
          logging.info("CUDA is not available. Defaulting to CPUs")
  
      # get form fields
      model_name = request.form["model_names_select"]
      use_gpu_if_available = request.form.get("use_gpu_ckbx", "off")
  
      # get nemo model from user (if not none)
      nemo_model_file = request.files.get("nemo_model", "")
  
      # if nemo model is not None, upload it to model cache
      if nemo_model_file != "":
          model_name = _store_model(nemo_model_file)
  
          # Alert user that model has been uploaded into the model cache,
          # and they should refresh the page to access the model
          result = render_template(
              "toast_msg.html",
              toast_message=f"Model {model_name} has been uploaded. "
              f"Refresh page !",
              timeout=5000,
          )
  
      else:
          # Alert user that model has been loaded onto a workers memory
          result = render_template(
              "toast_msg.html",
              toast_message=f"Model {model_name} has been initialized !",
              timeout=2000,
          )
  
      # Load model into memory cache
      model_api.initialize_model(model_name=model_name)
  
      # reset file banner
      reset_nemo_model_file_script = """
          <script>
              document.getElementById('nemo_model_file').value = ""
          </script>
      """
  
      result = result + reset_nemo_model_file_script
      result = make_response(result)
  
      # set cookies
      result.set_cookie("model_name", model_name)
      result.set_cookie("use_gpu", use_gpu_if_available)
      return result
  
imports: import atexit
  import glob
  import os
  import shutil
  import time
  from html import unescape
  from uuid import uuid4
  import model_api
  import torch
  import werkzeug
  from flask import Flask, make_response, render_template, request, url_for
  from flask_cors import CORS
  from werkzeug.utils import secure_filename
  from nemo.utils import logging
label_desc: Use image node_id 1 for calling a global function with example usage: initialize_model() and returns: result