class_docstr: Class representing a remote function stored in S3.
docstr: Construct a StoredFunction object.
  
  Args:
      sagemaker_session: (sagemaker.session.Session): The underlying sagemaker session which
          AWS service calls are delegated to.
      s3_base_uri: the base uri to which serialized artifacts will be uploaded.
      s3_kms_key: KMS key used to encrypt artifacts uploaded to S3.
      hmac_key: Key used to encrypt serialized and deserialized function and arguments.
      context: Build or run context of a pipeline step.
docstr_tok: ["Construct","a","StoredFunction","object",".","Args",":","sagemaker_session",":","(","sagemaker.session.Session",")",":","The","underlying","sagemaker","session","which","AWS","service","calls","are","delegated","to",".","s3_base_uri",":","the","base","uri","to","which","serialized","artifacts","will","be","uploaded",".","s3_kms_key",":","KMS","key","used","to","encrypt","artifacts","uploaded","to","S3",".","hmac_key",":","Key","used","to","encrypt","serialized","and","deserialized","function","and","arguments",".","context",":","Build","or","run","context","of","a","pipeline","step","."]
code: def __init__(
      self,
      sagemaker_session: Session,
      s3_base_uri: str,
      hmac_key: str,
      s3_kms_key: str = None,
      context: Context = Context(),
  ):
      """Construct a StoredFunction object.
  
      Args:
          sagemaker_session: (sagemaker.session.Session): The underlying sagemaker session which
              AWS service calls are delegated to.
          s3_base_uri: the base uri to which serialized artifacts will be uploaded.
          s3_kms_key: KMS key used to encrypt artifacts uploaded to S3.
          hmac_key: Key used to encrypt serialized and deserialized function and arguments.
          context: Build or run context of a pipeline step.
      """
      self.sagemaker_session = sagemaker_session
      self.s3_base_uri = s3_base_uri
      self.s3_kms_key = s3_kms_key
      self.hmac_key = hmac_key
      self.context = context
  
      self.func_upload_path = s3_path_join(
          s3_base_uri, context.step_name, context.func_step_s3_dir
      )
      self.results_upload_path = s3_path_join(
          s3_base_uri, context.execution_id, context.step_name
      )
  
file_path: sagemaker-python-sdk/src/sagemaker/remote_function/core/stored_function.py
imports: from __future__ import absolute_import
  import os
  from dataclasses import dataclass
  from typing import Any
  from sagemaker.s3 import s3_path_join
  from sagemaker.remote_function import logging_config
  from sagemaker.remote_function.core.pipeline_variables import Context, resolve_pipeline_variables
  import sagemaker.remote_function.core.serialization
  from sagemaker.session import Session
label_desc: Use image node_id 1 to create a new StoredFunction object  with example: obj = StoredFunction(sagemaker_session, s3_base_uri, hmac_key, s3_kms_key, context)