class_docstr: A class for creating and interacting with SageMaker AutoML jobs.
docstr: Deploy a candidate to a SageMaker Inference Pipeline.
  
  Args:
      initial_instance_count (int): The initial number of instances to run
          in the ``Endpoint`` created from this ``Model``.
      instance_type (str): The EC2 instance type to deploy this Model to.
          For example, 'ml.p2.xlarge'.
      serializer (:class:`~sagemaker.serializers.BaseSerializer`): A
          serializer object, used to encode data for an inference endpoint
          (default: None). If ``serializer`` is not None, then
          ``serializer`` will override the default serializer. The
          default serializer is set by the ``predictor_cls``.
      deserializer (:class:`~sagemaker.deserializers.BaseDeserializer`): A
          deserializer object, used to decode data from an inference
          endpoint (default: None). If ``deserializer`` is not None, then
          ``deserializer`` will override the default deserializer. The
          default deserializer is set by the ``predictor_cls``.
      candidate (CandidateEstimator or dict): a CandidateEstimator used for deploying
          to a SageMaker Inference Pipeline. If None, the best candidate will
          be used. If the candidate input is a dict, a CandidateEstimator will be
          created from it.
      sagemaker_session (sagemaker.session.Session): A SageMaker Session
          object, used for SageMaker interactions (default: None). If not
          specified, the one originally associated with the ``AutoML`` instance is used.
      name (str): The pipeline model name. If None, a default model name will
          be selected on each ``deploy``.
      endpoint_name (str): The name of the endpoint to create (default:
          None). If not specified, a unique endpoint name will be created.
      tags (Optional[Tags]): The list of tags to attach to this
          specific endpoint.
      wait (bool): Whether the call should wait until the deployment of
          model completes (default: True).
      vpc_config (dict): Specifies a VPC that your training jobs and hosted models have
          access to. Contents include "SecurityGroupIds" and "Subnets".
      enable_network_isolation (bool): Isolates the training container. No inbound or
          outbound network calls can be made, except for calls between peers within a
          training cluster for distributed training. Default: False
      model_kms_key (str): KMS key ARN used to encrypt the repacked
          model archive file if the model is repacked
      predictor_cls (callable[string, sagemaker.session.Session]): A
          function to call to create a predictor (default: None). If
          specified, ``deploy()``  returns the result of invoking this
          function on the created endpoint name.
      inference_response_keys (list): List of keys for response content. The order of the
          keys will dictate the content order in the response.
      volume_size (int): The size, in GB, of the ML storage volume attached to individual
          inference instance associated with the production variant. Currenly only Amazon EBS
          gp2 storage volumes are supported.
      model_data_download_timeout (int): The timeout value, in seconds, to download and
          extract model data from Amazon S3 to the individual inference instance associated
          with this production variant.
      container_startup_health_check_timeout (int): The timeout value, in seconds, for your
          inference container to pass health check by SageMaker Hosting. For more information
          about health check see:
          https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-inference-code.html#your-algorithms-inference-algo-ping-requests
  
  Returns:
      callable[string, sagemaker.session.Session] or ``None``:
          If ``predictor_cls`` is specified, the invocation of ``self.predictor_cls`` on
          the created endpoint name. Otherwise, ``None``.
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code: def deploy(
      self,
      initial_instance_count,
      instance_type,
      serializer=None,
      deserializer=None,
      candidate=None,
      sagemaker_session=None,
      name=None,
      endpoint_name=None,
      tags=None,
      wait=True,
      vpc_config=None,
      enable_network_isolation=False,
      model_kms_key=None,
      predictor_cls=None,
      inference_response_keys=None,
      volume_size=None,
      model_data_download_timeout=None,
      container_startup_health_check_timeout=None,
  ):
      """Deploy a candidate to a SageMaker Inference Pipeline.
  
      Args:
          initial_instance_count (int): The initial number of instances to run
              in the ``Endpoint`` created from this ``Model``.
          instance_type (str): The EC2 instance type to deploy this Model to.
              For example, 'ml.p2.xlarge'.
          serializer (:class:`~sagemaker.serializers.BaseSerializer`): A
              serializer object, used to encode data for an inference endpoint
              (default: None). If ``serializer`` is not None, then
              ``serializer`` will override the default serializer. The
              default serializer is set by the ``predictor_cls``.
          deserializer (:class:`~sagemaker.deserializers.BaseDeserializer`): A
              deserializer object, used to decode data from an inference
              endpoint (default: None). If ``deserializer`` is not None, then
              ``deserializer`` will override the default deserializer. The
              default deserializer is set by the ``predictor_cls``.
          candidate (CandidateEstimator or dict): a CandidateEstimator used for deploying
              to a SageMaker Inference Pipeline. If None, the best candidate will
              be used. If the candidate input is a dict, a CandidateEstimator will be
              created from it.
          sagemaker_session (sagemaker.session.Session): A SageMaker Session
              object, used for SageMaker interactions (default: None). If not
              specified, the one originally associated with the ``AutoML`` instance is used.
          name (str): The pipeline model name. If None, a default model name will
              be selected on each ``deploy``.
          endpoint_name (str): The name of the endpoint to create (default:
              None). If not specified, a unique endpoint name will be created.
          tags (Optional[Tags]): The list of tags to attach to this
              specific endpoint.
          wait (bool): Whether the call should wait until the deployment of
              model completes (default: True).
          vpc_config (dict): Specifies a VPC that your training jobs and hosted models have
              access to. Contents include "SecurityGroupIds" and "Subnets".
          enable_network_isolation (bool): Isolates the training container. No inbound or
              outbound network calls can be made, except for calls between peers within a
              training cluster for distributed training. Default: False
          model_kms_key (str): KMS key ARN used to encrypt the repacked
              model archive file if the model is repacked
          predictor_cls (callable[string, sagemaker.session.Session]): A
              function to call to create a predictor (default: None). If
              specified, ``deploy()``  returns the result of invoking this
              function on the created endpoint name.
          inference_response_keys (list): List of keys for response content. The order of the
              keys will dictate the content order in the response.
          volume_size (int): The size, in GB, of the ML storage volume attached to individual
              inference instance associated with the production variant. Currenly only Amazon EBS
              gp2 storage volumes are supported.
          model_data_download_timeout (int): The timeout value, in seconds, to download and
              extract model data from Amazon S3 to the individual inference instance associated
              with this production variant.
          container_startup_health_check_timeout (int): The timeout value, in seconds, for your
              inference container to pass health check by SageMaker Hosting. For more information
              about health check see:
              https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-inference-code.html#your-algorithms-inference-algo-ping-requests
  
      Returns:
          callable[string, sagemaker.session.Session] or ``None``:
              If ``predictor_cls`` is specified, the invocation of ``self.predictor_cls`` on
              the created endpoint name. Otherwise, ``None``.
      """
      sagemaker_session = sagemaker_session or self.sagemaker_session
      model = self.create_model(
          name=name,
          sagemaker_session=sagemaker_session,
          candidate=candidate,
          inference_response_keys=inference_response_keys,
          vpc_config=vpc_config,
          enable_network_isolation=enable_network_isolation,
          model_kms_key=model_kms_key,
          predictor_cls=predictor_cls,
      )
  
      return model.deploy(
          initial_instance_count=initial_instance_count,
          instance_type=instance_type,
          serializer=serializer,
          deserializer=deserializer,
          endpoint_name=endpoint_name,
          kms_key=model_kms_key,
          tags=format_tags(tags),
          wait=wait,
          volume_size=volume_size,
          model_data_download_timeout=model_data_download_timeout,
          container_startup_health_check_timeout=container_startup_health_check_timeout,
      )
  
imports: from __future__ import absolute_import
  import logging
  from typing import Optional, List, Dict
  from six import string_types
  from sagemaker import Model, PipelineModel, s3
  from sagemaker.automl.candidate_estimator import CandidateEstimator
  from sagemaker.config import AUTO_ML_ROLE_ARN_PATH, AUTO_ML_KMS_KEY_ID_PATH, AUTO_ML_VPC_CONFIG_PATH, AUTO_ML_VOLUME_KMS_KEY_ID_PATH, AUTO_ML_INTER_CONTAINER_ENCRYPTION_PATH
  from sagemaker.job import _Job
  from sagemaker.session import Session
  from sagemaker.utils import name_from_base, resolve_value_from_config, format_tags, Tags
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.workflow.pipeline_context import runnable_by_pipeline
label_desc: Use image node_id 8 for calling the AutoML obj's underlying member method code with example usage: obj.deploy(initial_instance_count, instance_type, serializer, deserializer, candidate, sagemaker_session, name, endpoint_name, tags, wait, vpc_config, enable_network_isolation, model_kms_key, predictor_cls, inference_response_keys, volume_size, model_data_download_timeout, container_startup_health_check_timeout) and returns: model