class_docstr: Base class for feature extractors.
class_docstr_tok: ["Base","class","for","feature","extractors","."]
docstr: Computes features for each time point for each time series.
  
  Parameters
  ----------
  series
      A SeriesBatch containing sequences of size [batch, sequence length, n_input],
      where n_input is typically 1 + * which corresponds to the univariate time series itself
      and additional time features, e.g. relative time.
  
  Returns
  -------
  SeriesBatch
      The transformed batch with sequences of size [batch, sequence length, n_features].
docstr_tok: ["Computes","features","for","each","time","point","for","each","time","series",".","Parameters","--","--","--","--","--","series","A","SeriesBatch","containing","sequences","of","size","[","batch",",","sequence","length",",","n_input","]",",","where","n_input","is","typically","1","+","*","which","corresponds","to","the","univariate","time","series","itself","and","additional","time","features",",","e.g",".","relative","time",".","Returns","--","--","--","-","SeriesBatch","The","transformed","batch","with","sequences","of","size","[","batch",",","sequence","length",",","n_features","]","."]
code: def forward(self, series: SeriesBatch) -> SeriesBatch:
      """
      Computes features for each time point for each time series.
  
      Parameters
      ----------
      series
          A SeriesBatch containing sequences of size [batch, sequence length, n_input],
          where n_input is typically 1 + * which corresponds to the univariate time series itself
          and additional time features, e.g. relative time.
  
      Returns
      -------
      SeriesBatch
          The transformed batch with sequences of size [batch, sequence length, n_features].
      """
  
file_path: gluonts/src/gluonts/nursery/few_shot_prediction/src/meta/models/EcDc/components/feature.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 FeatureExtractor obj's underlying member method code with example usage: obj.forward(series) without return types