class_docstr: Data model used for storing a batch of frames.
  Internally stored as a pandas DataFrame with columns
  "id" and "data".
  id: integer index of frame
  data: frame as np.array
  
  Arguments:
      frames (DataFrame): pandas Dataframe holding frames data
class_docstr_tok: ["Data","model","used","for","storing","a","batch","of","frames",".","Internally","stored","as","a","pandas","DataFrame","with","columns","''","id","''","and","``","data","''",".","id",":","integer","index","of","frame","data",":","frame","as","np.array","Arguments",":","frames","(","DataFrame",")",":","pandas","Dataframe","holding","frames","data"]
docstr: Aggregate batch based on method.
  Methods can be sum, count, min, max, mean
  
  Arguments:
      method: string with one of the five above options
docstr_tok: ["Aggregate","batch","based","on","method",".","Methods","can","be","sum",",","count",",","min",",","max",",","mean","Arguments",":","method",":","string","with","one","of","the","five","above","options"]
code: def aggregate(self, method: str) -> None:
      """
      Aggregate batch based on method.
      Methods can be sum, count, min, max, mean
  
      Arguments:
          method: string with one of the five above options
      """
      self._frames = self._frames.agg([method])
  
imports: from typing import Callable, Iterable, List, TypeVar, Union
  import numpy
  import pandas
  from evadb.expression.abstract_expression import ExpressionType
  from evadb.parser.alias import Alias
  from evadb.utils.generic_utils import PickleSerializer
  from evadb.utils.logging_manager import logger
label_desc: Use image node_id 40 for calling the Batch obj's underlying member method code with example usage: obj.aggregate(method) without return types