docstr: This function loads a previously saved transformation pipeline and model
  from the current active directory into the current python environment.
  Load object must be a pickle file.
  
  Example
  -------
  >>> saved_lr = load_model('lr_model_23122019')
  
  This will load the previously saved model in saved_lr variable. The file
  must be in the current directory.
  
  Parameters
  ----------
  model_name : str, default = none
      Name of pickle file to be passed as a string.
  
  platform: str, default = None
      Name of platform, if loading model from cloud. Current available options are:
      'aws', 'gcp' and 'azure'.
  
  authentication : dict
      dictionary of applicable authentication tokens.
  
      When platform = 'aws':
      {'bucket' : 'Name of Bucket on S3'}
  
      When platform = 'gcp':
      {'project': 'gcp_pycaret', 'bucket' : 'pycaret-test'}
  
      When platform = 'azure':
      {'container': 'pycaret-test'}
  
  verbose: bool, default = True
      Success message is not printed when verbose is set to False.
  
  Returns
  -------
  Model Object
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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 transformation pipeline and model
      from the current active directory into the current python environment.
      Load object must be a pickle file.
  
      Example
      -------
      >>> saved_lr = load_model('lr_model_23122019')
  
      This will load the previously saved model in saved_lr variable. The file
      must be in the current directory.
  
      Parameters
      ----------
      model_name : str, default = none
          Name of pickle file to be passed as a string.
  
      platform: str, default = None
          Name of platform, if loading model from cloud. Current available options are:
          'aws', 'gcp' and 'azure'.
  
      authentication : dict
          dictionary of applicable authentication tokens.
  
          When platform = 'aws':
          {'bucket' : 'Name of Bucket on S3'}
  
          When platform = 'gcp':
          {'project': 'gcp_pycaret', 'bucket' : 'pycaret-test'}
  
          When platform = 'azure':
          {'container': 'pycaret-test'}
  
      verbose: bool, default = True
          Success message is not printed when verbose is set to False.
  
      Returns
      -------
      Model Object
  
      """
  
      return pycaret.internal.persistence.load_model(
          model_name, platform, authentication, verbose
      )
  
imports: import inspect
  import os
  import warnings
  from collections import defaultdict
  from typing import Any, BinaryIO, Callable, Dict, Optional, Union
  import cloudpickle
  import pandas
  import pycaret.internal.patches.sklearn
  import pycaret.internal.patches.yellowbrick
  import pycaret.internal.persistence
  from pycaret.internal.logging import get_logger
  from pycaret.utils.constants import DATAFRAME_LIKE
  from pycaret.utils.generic import LazyExperimentMapping
label_desc: Use image node_id 15 for calling the _PyCaretExperiment obj's underlying member method code with example usage: obj.load_model(model_name, platform, authentication, verbose) and returns: pycaret