code: def load(path: str, map_location=None):
      """retrieve a fitted model from a .np file that was saved by save.
  
      Parameters
      ----------
          path : str
              path and filename to be saved. filename could be any but suggested to have extension .np.
          map_location : str, optional
              specifying the location where the model should be loaded.
              If you are running on a CPU-only machine, set map_location='cpu' to map your storages to the CPU.
              If you are running on CUDA, set map_location='cuda:device_id' (e.g. 'cuda:2').
              Default is None, which means the model is loaded to the same device as it was saved on.
      Returns
      -------
          np.forecaster.NeuralProphet
              previously saved model
  
      Examples
      --------
      Saved model could be loaded from disk file test_save_model.np
          >>> from neuralprophet import load
          >>> model = load("test_save_model.np")
      """
      torch_map_location = None
      if map_location is not None:
          torch_map_location = torch.device(map_location)
  
      m = torch.load(path, map_location=torch_map_location)
      m.restore_trainer(accelerator=map_location)
      return m
  
imports: from __future__ import annotations
  import logging
  import math
  import os
  import sys
  from collections import OrderedDict
  from typing import TYPE_CHECKING, Iterable, Optional, Union
  import numpy
  import pandas
  import pytorch_lightning
  import torch
  from neuralprophet import utils_torch
  from neuralprophet.hdays_utils import get_country_holidays
  from neuralprophet.logger import ProgressBar
label_desc: Use image node_id 2 for calling a global function with example usage: load(path, map_location) and returns: m