code: def download(
      model_type: str = "44khz",
      model_bitrate: str = "8kbps",
      tag: str = "latest",
      local_path: Path = None,
  ):
      """
      Downloads a specified model file based on model type, bitrate, and tag, saving it to a local path.
  
      Parameters
      ----------
      model_type : str, optional
          The type of model to download. Can be '44khz', '24khz', or '16khz'. Default is '44khz'.
      model_bitrate : str, optional
          The bitrate of the model. Can be '8kbps' or '16kbps'. Default is '8kbps'.
      tag : str, optional
          A specific version tag for the model. Default is 'latest'.
      local_path : Path, optional
          The local file path where the model will be saved. If not provided, a default path will be used.
  
      Returns
      -------
      Path
          The local path where the model is saved.
  
      Raises
      ------
      ValueError
          If the model type or bitrate is not supported, or if the model cannot be found or downloaded.
      """
  
      model_type = model_type.lower()
      tag = tag.lower()
  
      assert model_type in [
          "44khz",
          "24khz",
          "16khz",
      ], "model_type must be one of '44khz', '24khz', or '16khz'"
  
      assert model_bitrate in [
          "8kbps",
          "16kbps",
      ], "model_bitrate must be one of '8kbps', or '16kbps'"
  
      if tag == "latest":
          tag = __MODEL_LATEST_TAGS__[(model_type, model_bitrate)]
  
      download_link = __MODEL_URLS__.get(
          (model_type, tag, model_bitrate), None
      )
      logger.info(f"Download link: {download_link}")
  
      if download_link is None:
          raise ValueError(
              f"Could not find model with tag {tag} and model type {model_type}"
          )
  
      if local_path is None:
          local_path = (
              Path.home()
              / f".cache/descript/dac/weights_{model_type}_{model_bitrate}_{tag}.pth"
          )
  
      if not local_path.exists():
          local_path.parent.mkdir(parents=True, exist_ok=True)
  
          # Download the model
          import requests
  
          response = requests.get(download_link)
  
          if response.status_code != 200:
              raise ValueError(
                  f"Could not download model. Received response code {response.status_code}"
              )
          local_path.write_bytes(response.content)
  
      return local_path
  
imports: import math
  from pathlib import Path
  from typing import List, Union
  import logging
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils.parametrizations import weight_norm
label_desc: Use image node_id 4 for calling a global function with example usage: download(model_type, model_bitrate, tag, local_path) and returns: local_path