class_docstr: Represents a problem and its associated data. For instance, binary classification on the adult dataset.
docstr: Returns a named measure as a scalar value.
  
  Args:
      name (str): Name of measure.
  
  Returns:
      Union[Number, str, dict]: Scalar value associated with named measure.
docstr_tok: ["Returns","a","named","measure","as","a","scalar","value",".","Args",":","name","(","str",")",":","Name","of","measure",".","Returns",":","Union","[","Number",",","str",",","dict","]",":","Scalar","value","associated","with","named","measure","."]
code: def scalar_measure(self, name: str) -> Union[Number, str, dict]:
      """Returns a named measure as a scalar value.
  
      Args:
          name (str): Name of measure.
  
      Returns:
          Union[Number, str, dict]: Scalar value associated with named measure.
      """
      return self.measures[name].values["val"].iloc[0]
  
imports: import random
  from types import FunctionType
  import pandas
  from typing import Dict, Iterable
  from typing import Type, TypeVar
  from powerlift.bench.store import Store
  from typing import Union, Optional, List
  from dataclasses import dataclass
  from numbers import Number
  from powerlift.executors.base import Executor
  from powerlift.executors import LocalMachine
label_desc: Use image node_id 2 for calling the Task obj's underlying member method code with example usage: obj.scalar_measure(name) and returns: self