class_docstr: Cache Population Stage
class_docstr_tok: ["Cache","Population","Stage"]
docstr: This method will return output according to
  Implementation.
  
  :param input: Your input data.
  :param kwargs: A dictionary of keyword arguments.
      - 'logger' (any): The logger for logging.
      - 'config' (Config): Global configurations for the test
      - 'context' (any): The execution context.
  
  :return: The result of the execution.
docstr_tok: ["This","method","will","return","output","according","to","Implementation",".",":","param","input",":","Your","input","data",".",":","param","kwargs",":","A","dictionary","of","keyword","arguments",".","-","'logger","'","(","any",")",":","The","logger","for","logging",".","-","'config","'","(","Config",")",":","Global","configurations","for","the","test","-","'context","'","(","any",")",":","The","execution","context",".",":","return",":","The","result","of","the","execution","."]
code: def execute(self, input: Any, **kwargs) -> Any:
      """
      This method will return output according to
      Implementation.
  
      :param input: Your input data.
      :param kwargs: A dictionary of keyword arguments.
          - 'logger' (any): The logger for logging.
          - 'config' (Config): Global configurations for the test
          - 'context' (any): The execution context.
  
      :return: The result of the execution.
      """
      pipeline_context: PipelineContext = kwargs.get("context")
  
      code = input
  
      if (
          pipeline_context.config.enable_cache
          and pipeline_context.cache
      ):
          pipeline_context.cache.set(
              pipeline_context.cache.get_cache_key(pipeline_context),
              code,
          )
  
      return code
  
imports: from typing import Any
  from ..base_logic_unit import BaseLogicUnit
  from ..pipeline_context import PipelineContext
label_desc: Use image node_id 1 for calling the CachePopulation obj's underlying member method code with example usage: obj.execute(input) and returns: code