original_docstring: 
      Function for saving the model in the expected directory for SageMaker.
      
      Parameters
      ----------
      model: a Scikit-Learn estimator
      model_dir: A string that represents the path where the training job writes the model artifacts to. After training, artifacts in this directory are uploaded to S3 for model hosting. (this should be the default SageMaker environment variables)
      
docstring: Function for saving the model in the expected directory for SageMaker.
  Parameters
  
  a Scikit-Learn estimator
  model_dir: A string that represents the path where the training job writes the model artifacts to. After training, artifacts in this directory are uploaded to S3 for model hosting. (this should be the default SageMaker environment variables)
docstring_tokens: Function for saving the model in the expected directory for SageMaker . Parameters a Scikit - Learn estimator model_dir : A string that represents the path where the training job writes the model artifacts to . After training artifacts in this directory are uploaded to S3 for model hosting . ( this should be the default SageMaker environment variables )
code: def save_model(model, model_dir):
      '''
      Function for saving the model in the expected directory for SageMaker.
      
      Parameters
      ----------
      model: a Scikit-Learn estimator
      model_dir: A string that represents the path where the training job writes the model artifacts to. After training, artifacts in this directory are uploaded to S3 for model hosting. (this should be the default SageMaker environment variables)
      '''
      logger.info("Saving the model.")
                                                                
      # Print the coefficients of the trained classifier, and save the coefficients
      joblib.dump(model, os.path.join(model_dir, "model.joblib"))
code_tokens: def save_model ( model , model_dir ) : logger . info ( "Saving the model." ) joblib . dump ( model , os . path . join ( model_dir , "model.joblib" ) )
short_docstring: Function for saving the model in the expected directory for SageMaker.
short_docstring_tokens: Function for saving the model in the expected directory for SageMaker .
comment: '''
      Function for saving the model in the expected directory for SageMaker.
      
      Parameters
      ----------
      model: a Scikit-Learn estimator
      model_dir: A string that represents the path where the training job writes the model artifacts to. After training, artifacts in this directory are uploaded to S3 for model hosting. (this should be the default SageMaker environment variables)
      '''$SEP$# Print the coefficients of the trained classifier, and save the coefficients