class_docstr: Abstract class representing predictions.
class_docstr_tok: ["Abstract","class","representing","predictions","."]
docstr: Return a new Forecast object with a time series aggregated over the
  dimension axis.
  
  Parameters
  ----------
  agg_fun
      Aggregation function that defines the aggregation operation
      (typically mean or sum).
code: def copy_aggregate(self, agg_fun: Callable):
      """
      Return a new Forecast object with a time series aggregated over the
      dimension axis.
  
      Parameters
      ----------
      agg_fun
          Aggregation function that defines the aggregation operation
          (typically mean or sum).
      """
      raise NotImplementedError()
  
imports: import re
  import logging
  from dataclasses import field
  from typing import Callable, Dict, List, Optional, Union, Tuple
  import numpy
  import pandas
  from gluonts.core.component import validated
  from gluonts.pydantic import dataclass
  from gluonts import maybe
label_desc: Use image node_id 11 for calling the Forecast obj's underlying member method code with example usage: obj.copy_aggregate(agg_fun) without return types