class_docstr: Handle end-to-end training and deployment of custom RLEstimator code.
class_docstr_tok: ["Handle","end-to-end","training","and","deployment","of","custom","RLEstimator","code","."]
docstr: Return hyperparameters used by your custom TensorFlow code during model training.
docstr_tok: ["Return","hyperparameters","used","by","your","custom","TensorFlow","code","during","model","training","."]
code: def hyperparameters(self):
      """Return hyperparameters used by your custom TensorFlow code during model training."""
      hyperparameters = super(RLEstimator, self).hyperparameters()
  
      additional_hyperparameters = {
          SAGEMAKER_OUTPUT_LOCATION: self.output_path,
          # TODO: can be applied to all other estimators
          SAGEMAKER_ESTIMATOR: SAGEMAKER_ESTIMATOR_VALUE,
      }
  
      hyperparameters.update(
          EstimatorBase._json_encode_hyperparameters(
              additional_hyperparameters
          )
      )
      return hyperparameters
  
imports: from __future__ import absolute_import
  import enum
  import logging
  import re
  from typing import Union, Optional, List, Dict
  from sagemaker import image_uris, fw_utils
  from sagemaker.estimator import Framework, EstimatorBase
  from sagemaker.model import FrameworkModel, SAGEMAKER_OUTPUT_LOCATION
  from sagemaker.mxnet.model import MXNetModel
  from sagemaker.tensorflow.model import TensorFlowModel
  from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
  from sagemaker.workflow.entities import PipelineVariable
label_desc: Use image node_id 5 for calling the RLEstimator obj's underlying member method code with example usage: obj.hyperparameters() and returns: hyperparameters