code: def select_plotting_backend(model, plotting_backend):
      """Automatically selects the plotting backend based on the global plotting_backend and plotting_backend set by the
      user. If the plotting backend is selected as "plotly-resampler", triggers warning message.
  
      Parameters
      ----------
      model: NeuralProphet
          The configured model.
      plotting_backend: str
          The plotting backend to use.
  
      Returns
      -------
      str
          The new plotting backend.
      """
      if (
          hasattr(model, "plotting_backend")
          and plotting_backend is None
      ):
          plotting_backend = model.plotting_backend
          if plotting_backend == "plotly-resampler":
              validate_current_env_for_resampler()
      else:
          if plotting_backend is None:
              if validate_current_env_for_resampler(auto=True):
                  plotting_backend = "plotly-resampler"
              else:
                  plotting_backend = "plotly"
          elif plotting_backend == "plotly-resampler":
              validate_current_env_for_resampler()
      return plotting_backend.lower()
  
file_path: neural_prophet/neuralprophet/plot_utils.py
imports: import logging
  import warnings
  from collections import OrderedDict
  from typing import Optional
  import numpy
  import torch
  from neuralprophet import time_dataset, utils_torch
label_desc: Use image node_id 11 for calling a global function with example usage: select_plotting_backend(model, plotting_backend) and returns: plotting_backend