class_docstr: Represents a problem and its associated data. For instance, binary classification on the adult dataset.
docstr: Returns assets as a list of objects that maps to the aliases provided.
  
  Args:
      aliases (Iterable[str]): Aliases of assets to be retrieved.
  
  Returns:
      List[object]: List of objects retrieved by aliases.
docstr_tok: ["Returns","assets","as","a","list","of","objects","that","maps","to","the","aliases","provided",".","Args",":","aliases","(","Iterable","[","str","]",")",":","Aliases","of","assets","to","be","retrieved",".","Returns",":","List","[","object","]",":","List","of","objects","retrieved","by","aliases","."]
code: def data(self, aliases: Iterable[str]) -> List[object]:
      """Returns assets as a list of objects that maps to the aliases provided.
  
      Args:
          aliases (Iterable[str]): Aliases of assets to be retrieved.
  
      Returns:
          List[object]: List of objects retrieved by aliases.
      """
      from powerlift.bench.store import BytesParser
  
      outputs = []
      alias_map = self.config["aliases"]
      name_to_asset = {asset.name: asset for asset in self.assets}
      for alias in aliases:
          name = alias_map[alias]
          asset = name_to_asset[name]
          parsed = BytesParser.deserialize(
              asset.mimetype, asset.embedded
          )
          outputs.append(parsed)
      return outputs
  
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 3 for calling the Task obj's underlying member method code with example usage: obj.data(aliases) and returns: outputs