docstr: This function loads a previously saved pipeline.
  
  Example
  -------
  >>> from pycaret.regression import load_model
  >>> saved_lr = load_model('saved_lr_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' : 'Name of Bucket on S3', 'path': (optional) folder name under the bucket}
  
      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
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code: def load_model(
      self,
      model_name: str,
      platform: Optional[str] = None,
      authentication: Optional[Dict[str, str]] = None,
      verbose: bool = True,
  ):
      """
      This function loads a previously saved pipeline.
  
      Example
      -------
      >>> from pycaret.regression import load_model
      >>> saved_lr = load_model('saved_lr_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' : 'Name of Bucket on S3', 'path': (optional) folder name under the bucket}
  
          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
  
      """
  
      return super().load_model(
          model_name=model_name,
          platform=platform,
          authentication=authentication,
          verbose=verbose,
      )
  
imports: import logging
  import re
  import time
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import pandas
  from joblib.memory import Memory
  from pycaret.containers.metrics.regression import get_all_metric_containers
  from pycaret.containers.models.regression import ALL_ALLOWED_ENGINES, get_all_model_containers, get_container_default_engines
  from pycaret.internal.display import CommonDisplay
  from pycaret.internal.logging import get_logger
  from pycaret.internal.parallel.parallel_backend import ParallelBackend
  from pycaret.internal.pipeline import Pipeline
  from pycaret.internal.preprocess.preprocessor import Preprocessor
  from pycaret.internal.pycaret_experiment.non_ts_supervised_experiment import _NonTSSupervisedExperiment
  from pycaret.loggers.base_logger import BaseLogger
  from pycaret.utils.constants import DATAFRAME_LIKE, SEQUENCE_LIKE, TARGET_LIKE
  from pycaret.utils.generic import MLUsecase, highlight_setup
label_desc: Use image node_id 19 for calling the RegressionExperiment obj's underlying member method code with example usage: obj.load_model(model_name, platform, authentication, verbose) and returns: super