code: def upload(
      model,
      name: str = None,
      description: str = None,
      tasks: List[str] = None,
      details: Dict = None,
      path=None,
      api_key: str = None,
  ):
      """
      Saves and deploys a model to Backprop.
  
      Args:
          model: Model object
          api_key: Backprop API key
          name: string identifier for the model. Lowercase letters and numbers.
              No spaces/special characters except dashes.
          description: String description of the model.
          tasks: List of supported task strings
          details: Valid json dictionary of additional details about the model
          path: Optional path to save model
  
      Example::
  
          import backprop
  
          tg = backprop.TextGeneration("t5_small")
  
          # Any text works as training data
          inp = ["I really liked the service I received!", "Meh, it was not impressive."]
          out = ["positive", "negative"]
  
          # Finetune with a single line of code
          tg.finetune({"input_text": inp, "output_text": out})
  
          # Use your trained model
          prediction = tg("I enjoyed it!")
  
          print(prediction)
          # Prints
          "positive"
  
          # Upload to Backprop for production ready inference
  
          model = tg.model
          # Describe your model
          name = "t5-sentiment"
          description = "Predicts positive and negative sentiment"
  
          backprop.upload(model, name=name, description=description, api_key="abc")
      """
  
      if api_key is None:
          raise ValueError("Please provide your api_key")
  
      print("Saving model...")
      path = save(
          model,
          name=name,
          description=description,
          tasks=tasks,
          details=details,
          path=path,
      )
  
      print("Testing that the model can be loaded...")
      # Loading model to get the model name
      with open(os.path.join(path, "model.bin"), "rb") as f:
          model = dill.load(f)
          model_name = model.name
  
      # Save working directory
      cwd = os.getcwd()
  
      # Move to model directory
      os.chdir(path)
  
      print("Creating zip...")
      zip_obj = ZipFile(f"{model_name}.zip", "w")
  
      zip_obj.write("config.json")
      zip_obj.write("requirements.txt")
      zip_obj.write("model.bin")
  
      zip_obj.close()
  
      print("Getting upload url...")
      res = requests.post(
          "https://api.backprop.co/upload-url",
          json={"model_name": model_name},
          headers={"x-api-key": api_key},
      )
      if res.status_code != 200:
          out = res.json().get("message")
  
          if out is None:
              out = res.json().get("error")
  
          raise Exception(f"Failed to get upload url: {out}")
  
      upload_url = res.json()
  
      print("Uploading to Backprop, this may take a few minutes...")
      with open(f"{model_name}.zip", "rb") as f:
          res = requests.put(upload_url, f)
  
      if res.status_code != 200:
          raise Exception(f"Failed to upload. Please try again.")
  
      print(
          "Successfully uploaded the model to Backprop. See the build process at https://dashboard.backprop.co"
      )
  
      # Move back to working directory
      os.chdir(cwd)
  
imports: from typing import Dict, List
  from .None import save
  from zipfile import ZipFile
  import os
  import dill
  import requests
label_desc: Use image node_id 1 for calling a global function with example usage: upload(model, name, description, tasks, details, path, api_key) without return types