class_docstr: A pipeline of SageMaker `Model` instances.
  
  This pipeline can be deployed as an `Endpoint` on SageMaker.
docstr: Return a ``Transformer`` that uses this Model.
  
  Args:
      instance_count (int): Number of EC2 instances to use.
      instance_type (str): Type of EC2 instance to use, for example,
          'ml.c4.xlarge'.
      strategy (str): The strategy used to decide how to batch records in
          a single request (default: None). Valid values: 'MultiRecord'
          and 'SingleRecord'.
      assemble_with (str): How the output is assembled (default: None).
          Valid values: 'Line' or 'None'.
      output_path (str): S3 location for saving the transform result. If
          not specified, results are stored to a default bucket.
      output_kms_key (str): Optional. KMS key ID for encrypting the
          transform output (default: None).
      accept (str): The accept header passed by the client to
          the inference endpoint. If it is supported by the endpoint,
          it will be the format of the batch transform output.
      env (dict): Environment variables to be set for use during the
          transform job (default: None).
      max_concurrent_transforms (int): The maximum number of HTTP requests
          to be made to each individual transform container at one time.
      max_payload (int): Maximum size of the payload in a single HTTP
          request to the container in MB.
      tags (list[dict]): List of tags for labeling a transform job. If
          none specified, then the tags used for the training job are used
          for the transform job.
      volume_kms_key (str): Optional. KMS key ID for encrypting the volume
          attached to the ML compute instance (default: None).
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code: def transformer(
      self,
      instance_count,
      instance_type,
      strategy=None,
      assemble_with=None,
      output_path=None,
      output_kms_key=None,
      accept=None,
      env=None,
      max_concurrent_transforms=None,
      max_payload=None,
      tags=None,
      volume_kms_key=None,
  ):
      """Return a ``Transformer`` that uses this Model.
  
      Args:
          instance_count (int): Number of EC2 instances to use.
          instance_type (str): Type of EC2 instance to use, for example,
              'ml.c4.xlarge'.
          strategy (str): The strategy used to decide how to batch records in
              a single request (default: None). Valid values: 'MultiRecord'
              and 'SingleRecord'.
          assemble_with (str): How the output is assembled (default: None).
              Valid values: 'Line' or 'None'.
          output_path (str): S3 location for saving the transform result. If
              not specified, results are stored to a default bucket.
          output_kms_key (str): Optional. KMS key ID for encrypting the
              transform output (default: None).
          accept (str): The accept header passed by the client to
              the inference endpoint. If it is supported by the endpoint,
              it will be the format of the batch transform output.
          env (dict): Environment variables to be set for use during the
              transform job (default: None).
          max_concurrent_transforms (int): The maximum number of HTTP requests
              to be made to each individual transform container at one time.
          max_payload (int): Maximum size of the payload in a single HTTP
              request to the container in MB.
          tags (list[dict]): List of tags for labeling a transform job. If
              none specified, then the tags used for the training job are used
              for the transform job.
          volume_kms_key (str): Optional. KMS key ID for encrypting the volume
              attached to the ML compute instance (default: None).
      """
      self._create_sagemaker_pipeline_model(instance_type)
  
      return Transformer(
          self.name,
          instance_count,
          instance_type,
          strategy=strategy,
          assemble_with=assemble_with,
          output_path=output_path,
          output_kms_key=output_kms_key,
          accept=accept,
          max_concurrent_transforms=max_concurrent_transforms,
          max_payload=max_payload,
          env=env,
          tags=format_tags(tags),
          base_transform_job_name=self.name,
          volume_kms_key=volume_kms_key,
          sagemaker_session=self.sagemaker_session,
      )
  
imports: from __future__ import absolute_import
  from typing import Optional, Dict, List, Union
  import sagemaker
  from sagemaker import ModelMetrics, Model
  from sagemaker.config import ENDPOINT_CONFIG_KMS_KEY_ID_PATH, MODEL_VPC_CONFIG_PATH, MODEL_ENABLE_NETWORK_ISOLATION_PATH, MODEL_EXECUTION_ROLE_ARN_PATH, load_sagemaker_config
  from sagemaker.drift_check_baselines import DriftCheckBaselines
  from sagemaker.metadata_properties import MetadataProperties
  from sagemaker.session import Session
  from sagemaker.utils import name_from_image, update_container_with_inference_params, resolve_value_from_config, format_tags
  from sagemaker.transformer import Transformer
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.workflow.pipeline_context import runnable_by_pipeline
  from sagemaker.utils import instance_supports_kms
label_desc: Use image node_id 7 for calling the PipelineModel obj's underlying member method code with example usage: obj.transformer(instance_count, instance_type, strategy, assemble_with, output_path, output_kms_key, accept, env, max_concurrent_transforms, max_payload, tags, volume_kms_key) and returns: Transformer