class_docstr: Code Execution 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")
      logger: Logger = kwargs.get("logger")
  
      code = input
      retry_count = 0
      code_to_run = code
      result = None
      while retry_count < pipeline_context.config.max_retries:
          try:
              # Execute the code
              code_context = CodeExecutionContext(
                  pipeline_context.get_intermediate_value(
                      "last_prompt_id"
                  ),
                  pipeline_context.get_intermediate_value("skills"),
              )
  
              result = pipeline_context.query_exec_tracker.execute_func(
                  pipeline_context.get_intermediate_value(
                      "code_manager"
                  ).execute_code,
                  code=code_to_run,
                  context=code_context,
              )
  
              if output_helper := pipeline_context.get_intermediate_value(
                  "output_type_helper"
              ):
                  (
                      validation_ok,
                      validation_errors,
                  ) = output_helper.validate(result)
  
                  if not validation_ok:
                      raise InvalidLLMOutputType(validation_errors)
  
              break
  
          except Exception as e:
              if (
                  not pipeline_context.config.use_error_correction_framework
                  or retry_count
                  >= pipeline_context.config.max_retries - 1
              ):
                  raise e
  
              retry_count += 1
  
              logger.log(
                  f"Failed to execute code with a correction framework "
                  f"[retry number: {retry_count}]",
                  level=logging.WARNING,
              )
  
              traceback_error = traceback.format_exc()
  
              # Get Error Prompt for retry
              error_prompt = self._get_error_prompt(e)
              code_to_run = (
                  pipeline_context.query_exec_tracker.execute_func(
                      self._retry_run_code,
                      code,
                      pipeline_context,
                      logger,
                      traceback_error,
                      error_prompt,
                  )
              )
  
      return result
  
file_path: pandas-ai/pandasai/pipelines/smart_datalake_chat/code_execution.py
imports: import logging
  import traceback
  from typing import Any, List
  from pandasai.exceptions import InvalidLLMOutputType
  from pandasai.prompts.base import AbstractPrompt
  from pandasai.prompts.correct_output_type_error_prompt import CorrectOutputTypeErrorPrompt
  from ...helpers.code_manager import CodeExecutionContext
  from ...helpers.logger import Logger
  from ..base_logic_unit import BaseLogicUnit
  from ..pipeline_context import PipelineContext
  from ...prompts.correct_error_prompt import CorrectErrorPrompt
label_desc: Use image node_id 1 for calling the CodeExecution obj's underlying member method code with example usage: obj.execute(input) and returns: result