code: def choose_variables(
      required_variables: Union[bool, int, list],
      variables_of_interest: List,
  ):
      """
      This method provides a way to choose the confounders whose values we wish to
      modify for finding its effect on the ability of the treatment to affect the outcome.
      """
  
      invert = None
  
      if required_variables is False:
          logger.info(
              "All variables required: Running bootstrap adding noise to confounders, instrumental variables and effect modifiers."
          )
          return None
  
      elif required_variables is True:
          logger.info(
              "All variables required: Running bootstrap adding noise to confounders, instrumental variables and effect modifiers."
          )
          return variables_of_interest
  
      elif type(required_variables) is int:
          if len(variables_of_interest) < required_variables:
              logger.error(
                  "Too many variables passed.\n The number of  variables is: {}.\n The number of variables passed: {}".format(
                      len(variables_of_interest), required_variables
                  )
              )
              raise ValueError(
                  "The number of variables in the required_variables is greater than the number of confounders, instrumental variables and effect modifiers"
              )
          else:
              # Shuffle the confounders
              return random.sample(
                  variables_of_interest, required_variables
              )
  
      elif type(required_variables) is list:
          # Check if all are select or deselect variables
          if all(variable[0] == "-" for variable in required_variables):
              invert = True
              required_variables = [
                  variable[1:] for variable in required_variables
              ]
          elif all(
              variable[0] != "-" for variable in required_variables
          ):
              invert = False
          else:
              logger.error(
                  "{} has both select and delect variables".format(
                      required_variables
                  )
              )
              raise ValueError(
                  "It appears that there are some select and deselect variables. Note you can either select or delect variables at a time, but not both"
              )
  
          # Check if all the required_variables belong to confounders, instrumental variables or effect
          if set(required_variables) - set(
              variables_of_interest
          ) != set([]):
              logger.error(
                  "{} are not confounder, instrumental variable or effect modifier".format(
                      list(
                          set(required_variables)
                          - set(variables_of_interest)
                      )
                  )
              )
              raise ValueError(
                  "At least one of required_variables is not a valid variable name, or it is not a confounder, instrumental variable or effect modifier"
              )
  
          if invert is False:
              return required_variables
          elif invert is True:
              return list(
                  set(variables_of_interest) - set(required_variables)
              )
  
imports: import logging
  import random
  from enum import Enum
  from typing import List, Union
  import numpy
  import scipy.stats
  from dowhy.utils.api import parse_state
label_desc: Use image node_id 1 for calling a global function with example usage: choose_variables(required_variables, variables_of_interest) and returns: None, variables_of_interest, random, required_variables, list