code: def load_model(
      model_name: str,
      platform: Optional[str] = None,
      authentication: Optional[Dict[str, str]] = None,
      verbose: bool = True,
  ):
      """
      This function loads a previously saved pipeline/model.
  
      Example
      -------
      >>> from pycaret.time_series import load_model
      >>> saved_arima = load_model('saved_arima_model')
  
  
      model_name: str
          Name of the model.
  
  
      platform: str, default = None
          Name of the cloud platform. Currently supported platforms:
          'aws', 'gcp' and 'azure'.
  
  
      authentication: dict, default = None
          dictionary of applicable authentication tokens.
  
          when platform = 'aws':
          {'bucket' : 'S3-bucket-name'}
  
          when platform = 'gcp':
          {'project': 'gcp-project-name', 'bucket' : 'gcp-bucket-name'}
  
          when platform = 'azure':
          {'container': 'azure-container-name'}
  
  
      verbose: bool, default = True
          Success message is not printed when verbose is set to False.
  
  
      Returns:
          Trained Model
  
      """
  
      experiment = _CURRENT_EXPERIMENT
      if experiment is None:
          experiment = _EXPERIMENT_CLASS()
  
      return experiment.load_model(
          model_name=model_name,
          platform=platform,
          authentication=authentication,
          verbose=verbose,
      )
  
imports: import logging
  import os
  from typing import TYPE_CHECKING, Any, BinaryIO, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import pandas
  from pycaret.internal.parallel.parallel_backend import ParallelBackend
  from pycaret.time_series.forecasting.oop import TSForecastingExperiment
  from pycaret.utils.generic import check_if_global_is_not_none
label_desc: Use image node_id 13 for calling a global function with example usage: load_model(model_name, platform, authentication, verbose) and returns: experiment