class_docstr: Handle end-to-end training and deployment of user-provided TensorFlow code.
class_docstr_tok: ["Handle","end-to-end","training","and","deployment","of","user-provided","TensorFlow","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(TensorFlow, self).hyperparameters()
      additional_hyperparameters = self._distribution_configuration(
          self.distribution
      )
  
      if self.model_dir is not False:
          self.model_dir = self.model_dir or self._default_s3_path(
              "model",
              mpi=additional_hyperparameters.get(
                  self.LAUNCH_MPI_ENV_NAME, False
              ),
          )
          additional_hyperparameters["model_dir"] = self.model_dir
  
      hyperparameters.update(
          EstimatorBase._json_encode_hyperparameters(
              additional_hyperparameters
          )
      )
  
      if self.compiler_config:
          training_compiler_hyperparameters = (
              self.compiler_config._to_hyperparameter_dict()
          )
          hyperparameters.update(
              EstimatorBase._json_encode_hyperparameters(
                  training_compiler_hyperparameters
              )
          )
  
      return hyperparameters
  
imports: from __future__ import absolute_import
  import logging
  from typing import Optional, Union, Dict
  from packaging import version
  from sagemaker import image_uris, s3, utils
  from sagemaker.deprecations import renamed_kwargs
  from sagemaker.estimator import Framework, EstimatorBase
  import sagemaker.fw_utils
  from sagemaker.tensorflow import defaults
  from sagemaker.tensorflow.model import TensorFlowModel
  from sagemaker.transformer import Transformer
  from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
  from sagemaker.workflow import is_pipeline_variable
  from sagemaker.tensorflow.training_compiler.config import TrainingCompilerConfig
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.utils import format_tags
label_desc: Use image node_id 7 for calling the TensorFlow obj's underlying member method code with example usage: obj.hyperparameters() and returns: hyperparameters