class_docstr: Base class for decoders.
class_docstr_tok: ["Base","class","for","decoders","."]
docstr: Forecast from encoded (query, support set) (typically via attention) and encoded queries.
  
  Parameters
  ----------
  value: Encoded (query, support set).
  query: Encoded query.
  
  Returns
  -------
  torch.Tensor: predictions of size [n_q, *, *]
docstr_tok: ["Forecast","from","encoded","(","query",",","support","set",")","(","typically","via","attention",")","and","encoded","queries",".","Parameters","--","--","--","--","--","value",":","Encoded","(","query",",","support","set",")",".","query",":","Encoded","query",".","Returns","--","--","--","-","torch.Tensor",":","predictions","of","size","[","n_q",",","*",",","*","]"]
code: def forward(
      self, query: torch.Tensor, value: torch.Tensor
  ) -> torch.Tensor:
      """
      Forecast from encoded (query, support set) (typically via attention) and encoded queries.
  
      Parameters
      ----------
      value: Encoded (query, support set).
      query: Encoded query.
  
      Returns
      -------
      torch.Tensor: predictions of size [n_q, *, *]
      """
  
file_path: gluonts/src/gluonts/nursery/few_shot_prediction/src/meta/models/EcDc/components/decoder.py
imports: from abc import ABC, abstractmethod
  import torch
  from torch import nn
  import torch.nn.functional
label_desc: Use image node_id 1 for calling the Decoder obj's underlying member method code with example usage: obj.forward(query, value) without return types