class_docstr: Base class for all meta models that make predictions based on a support set and queries
class_docstr_tok: ["Base","class","for","all","meta","models","that","make","predictions","based","on","a","support","set","and","queries"]
docstr: Computes the forecasts for query past from the support set.
  
  Parameters
  ----------
  supps
      The support set.
  query
      The queries to be forecasted consisting.
  
  Returns
  -------
  torch.Tensor
      The forecasts for the entire prediction length with shape `[num_queries,
      prediction_length, *]`.
docstr_tok: ["Computes","the","forecasts","for","query","past","from","the","support","set",".","Parameters","--","--","--","--","--","supps","The","support","set",".","query","The","queries","to","be","forecasted","consisting",".","Returns","--","--","--","-","torch.Tensor","The","forecasts","for","the","entire","prediction","length","with","shape","`","[","num_queries",",","prediction_length",",","*","]","`","."]
code: def forward(
      self, supps: SeriesBatch, query: SeriesBatch
  ) -> torch.Tensor:
      """
      Computes the forecasts for query past from the support set.
  
      Parameters
      ----------
      supps
          The support set.
      query
          The queries to be forecasted consisting.
  
      Returns
      -------
      torch.Tensor
          The forecasts for the entire prediction length with shape `[num_queries,
          prediction_length, *]`.
      """
  
file_path: gluonts/src/gluonts/nursery/few_shot_prediction/src/meta/models/model.py
imports: from abc import ABC, abstractmethod
  import torch
  from torch import nn
  from meta.data.batch import SeriesBatch
label_desc: Use image node_id 1 for calling the MetaModel obj's underlying member method code with example usage: obj.forward(supps, query) without return types