docstr: Execute the python code generated by LLMs to answer the question
  about the input dataframe. Run the code in the current context and return the
  result.
  
  Args:
      code (str): Python code to execute.
      context (CodeExecutionContext): Code Execution Context
              with prompt id and skills.
  
  Returns:
      Any: The result of the code execution. The type of the result depends
          on the generated code.
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code: def execute_code(
      self, code: str, context: CodeExecutionContext
  ) -> Any:
      """
      Execute the python code generated by LLMs to answer the question
      about the input dataframe. Run the code in the current context and return the
      result.
  
      Args:
          code (str): Python code to execute.
          context (CodeExecutionContext): Code Execution Context
                  with prompt id and skills.
  
      Returns:
          Any: The result of the code execution. The type of the result depends
              on the generated code.
  
      """
      code = self._replace_plot_png(code)
      self._current_code_executed = code
  
      # Add save chart code
      if self._config.save_charts:
          code = add_save_chart(
              code,
              logger=self._logger,
              file_name=str(context.prompt_id),
              save_charts_path_str=self._config.save_charts_path,
          )
      else:
          # Temporarily save generated chart to display
          code = add_save_chart(
              code,
              logger=self._logger,
              file_name="temp_chart",
              save_charts_path_str=find_project_root()
              + "/exports/charts",
          )
  
      # Reset used skills
      context.skills_manager.used_skills = []
  
      # Get the code to run removing unsafe imports and df overwrites
      code_to_run = self._clean_code(code, context)
      self.last_code_executed = code_to_run
      self._logger.log(
          f"""
  Code running:
  ```
  {code_to_run}
          ```"""
      )
  
      # List the required dfs, so we can avoid to run the connectors
      # if the code does not need them
      dfs = self._required_dfs(code_to_run)
      environment: dict = self._get_environment()
      environment["dfs"] = self._get_originals(dfs)
  
      if self._validate_direct_sql(self._dfs):
          environment["execute_sql_query"] = self._dfs[
              0
          ].get_query_exec_func()
  
      # Add skills to the env
      if context.skills_manager.used_skills:
          for skill_func_name in context.skills_manager.used_skills:
              skill = context.skills_manager.get_skill_by_func_name(
                  skill_func_name
              )
              environment[skill_func_name] = skill
  
      # Execute the code
      exec(code_to_run, environment)
  
      # Get the result
      if "result" not in environment:
          raise NoResultFoundError("No result returned")
  
      return environment["result"]
  
imports: import re
  import ast
  import uuid
  from collections import defaultdict
  import astor
  import pandas
  from pandasai.helpers.path import find_project_root
  from pandasai.helpers.skills_manager import SkillsManager
  from pandasai.helpers.sql import extract_table_names
  from .node_visitors import AssignmentVisitor, CallVisitor
  from .save_chart import add_save_chart
  from .optional import import_dependency
  from ..exceptions import BadImportError, MaliciousQueryError, NoResultFoundError, InvalidConfigError
  from ..constants import WHITELISTED_BUILTINS, WHITELISTED_LIBRARIES
  from typing import Union, List, Generator, Any
  from ..helpers.logger import Logger
  from ..schemas.df_config import Config
  import logging
  import traceback
label_desc: Use image node_id 5 for calling the CodeManager obj's underlying member method code with example usage: obj.execute_code(code, context) and returns: environment