class_docstr: Handle end-to-end training and deployment of custom Chainer code.
docstr: Return hyperparameters used by your custom Chainer code during training.
docstr_tok: ["Return","hyperparameters","used","by","your","custom","Chainer","code","during","training","."]
code: def hyperparameters(self):
      """Return hyperparameters used by your custom Chainer code during training."""
      hyperparameters = super(Chainer, self).hyperparameters()
  
      additional_hyperparameters = {
          Chainer._use_mpi: self.use_mpi,
          Chainer._num_processes: self.num_processes,
          Chainer._process_slots_per_host: self.process_slots_per_host,
          Chainer._additional_mpi_options: self.additional_mpi_options,
      }
  
      # remove unset keys.
      additional_hyperparameters = {
          k: v for k, v in additional_hyperparameters.items() if v
      }
      hyperparameters.update(
          EstimatorBase._json_encode_hyperparameters(
              additional_hyperparameters
          )
      )
      return hyperparameters
  
file_path: sagemaker-python-sdk/src/sagemaker/chainer/estimator.py
imports: from __future__ import absolute_import
  import logging
  from typing import Union, Optional, Dict
  from sagemaker.estimator import Framework, EstimatorBase
  from sagemaker.fw_utils import framework_name_from_image, framework_version_from_tag, python_deprecation_warning, validate_version_or_image_args
  from sagemaker.chainer import defaults
  from sagemaker.chainer.model import ChainerModel
  from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
  from sagemaker.workflow.entities import PipelineVariable
label_desc: Use image node_id 2 for calling the Chainer obj's underlying member method code with example usage: obj.hyperparameters() and returns: hyperparameters