class_docstr: Handle end-to-end training and deployment of custom Chainer code.
docstr: Create a SageMaker ``ChainerModel`` object that can be deployed to an ``Endpoint``.
  
  Args:
      model_server_workers (int): Optional. The number of worker processes
          used by the inference server. If None, server will use one
          worker per vCPU.
      role (str): The ``ExecutionRoleArn`` IAM Role ARN for the ``Model``,
          which is also used during transform jobs. If not specified, the
          role from the Estimator will be used.
      vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on
          the model. Default: use subnets and security groups from this Estimator.
  
          * 'Subnets' (list[str]): List of subnet ids.
          * 'SecurityGroupIds' (list[str]): List of security group ids.
  
      entry_point (str): Path (absolute or relative) to the local Python source file which
          should be executed as the entry point to training. If ``source_dir`` is specified,
          then ``entry_point`` must point to a file located at the root of ``source_dir``.
          If not specified, the training entry point is used.
      source_dir (str): Path (absolute or relative) to a directory with any other serving
          source code dependencies aside from the entry point file.
          If not specified, the model source directory from training is used.
      dependencies (list[str]): A list of paths to directories (absolute or relative) with
          any additional libraries that will be exported to the container.
          If not specified, the dependencies from training are used.
          This is not supported with "local code" in Local Mode.
      **kwargs: Additional kwargs passed to the ChainerModel constructor.
  
  Returns:
      sagemaker.chainer.model.ChainerModel: A SageMaker ``ChainerModel``
      object. See :func:`~sagemaker.chainer.model.ChainerModel` for full details.
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code: def create_model(
      self,
      model_server_workers=None,
      role=None,
      vpc_config_override=VPC_CONFIG_DEFAULT,
      entry_point=None,
      source_dir=None,
      dependencies=None,
      **kwargs,
  ):
      """Create a SageMaker ``ChainerModel`` object that can be deployed to an ``Endpoint``.
  
      Args:
          model_server_workers (int): Optional. The number of worker processes
              used by the inference server. If None, server will use one
              worker per vCPU.
          role (str): The ``ExecutionRoleArn`` IAM Role ARN for the ``Model``,
              which is also used during transform jobs. If not specified, the
              role from the Estimator will be used.
          vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on
              the model. Default: use subnets and security groups from this Estimator.
  
              * 'Subnets' (list[str]): List of subnet ids.
              * 'SecurityGroupIds' (list[str]): List of security group ids.
  
          entry_point (str): Path (absolute or relative) to the local Python source file which
              should be executed as the entry point to training. If ``source_dir`` is specified,
              then ``entry_point`` must point to a file located at the root of ``source_dir``.
              If not specified, the training entry point is used.
          source_dir (str): Path (absolute or relative) to a directory with any other serving
              source code dependencies aside from the entry point file.
              If not specified, the model source directory from training is used.
          dependencies (list[str]): A list of paths to directories (absolute or relative) with
              any additional libraries that will be exported to the container.
              If not specified, the dependencies from training are used.
              This is not supported with "local code" in Local Mode.
          **kwargs: Additional kwargs passed to the ChainerModel constructor.
  
      Returns:
          sagemaker.chainer.model.ChainerModel: A SageMaker ``ChainerModel``
          object. See :func:`~sagemaker.chainer.model.ChainerModel` for full details.
      """
      kwargs["name"] = self._get_or_create_name(kwargs.get("name"))
  
      if "image_uri" not in kwargs:
          kwargs["image_uri"] = self.image_uri
  
      return ChainerModel(
          self.model_data,
          role or self.role,
          entry_point or self._model_entry_point(),
          source_dir=(source_dir or self._model_source_dir()),
          container_log_level=self.container_log_level,
          code_location=self.code_location,
          py_version=self.py_version,
          framework_version=self.framework_version,
          model_server_workers=model_server_workers,
          sagemaker_session=self.sagemaker_session,
          vpc_config=self.get_vpc_config(vpc_config_override),
          dependencies=(dependencies or self.dependencies),
          **kwargs,
      )
  
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 3 for calling the Chainer obj's underlying member method code with example usage: obj.create_model(model_server_workers, role, vpc_config_override, entry_point, source_dir, dependencies) and returns: ChainerModel