class_docstr: This method should have a common interface such that it can be called after
  the encoder as well as after the decoder
class_docstr_tok: ["This","method","should","have","a","common","interface","such","that","it","can","be","called","after","the","encoder","as","well","as","after","the","decoder"]
code: def __init__(self, models, beam_size, record_attention=False):
      super().__init__()
      self.models = nn.ModuleList(models)
      self.incremental_states = {}
      self.beam_size = beam_size
      self.record_attention = record_attention
  
file_path: pytext/pytext/torchscript/seq2seq/decoder.py
imports: from typing import Dict, List, Tuple
  import torch
  import torch.jit
  import torch.jit.quantized
  from torch import nn, Tensor
label_desc: Use image node_id 1 to create a new DecoderBatchedStepEnsemble object from inherited base classes: nn with example: obj = DecoderBatchedStepEnsemble(models, beam_size, record_attention)