class_docstr: Base class flexible RNNs.
  
  Forward function can handle sequences of length 0.
docstr: Mask the hidden_state where there is no encoding.
  
  Args:
      hidden_state (HiddenState): hidden state where some entries need replacement
      no_encoding (torch.BoolTensor): positions that need replacement
      initial_hidden_state (HiddenState): hidden state to use for replacement
  
  Returns:
      HiddenState: hidden state with propagated initial hidden state where appropriate
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code: def handle_no_encoding(
      self,
      hidden_state: HiddenState,
      no_encoding: torch.BoolTensor,
      initial_hidden_state: HiddenState,
  ) -> HiddenState:
      """
      Mask the hidden_state where there is no encoding.
  
      Args:
          hidden_state (HiddenState): hidden state where some entries need replacement
          no_encoding (torch.BoolTensor): positions that need replacement
          initial_hidden_state (HiddenState): hidden state to use for replacement
  
      Returns:
          HiddenState: hidden state with propagated initial hidden state where appropriate
      """
      pass
  
imports: from abc import ABC, abstractmethod
  from typing import Tuple, Type, Union
  import torch
  from torch import nn
  from torch.nn.utils import rnn
label_desc: Use image node_id 1 for calling the RNN obj's underlying member method code with example usage: obj.handle_no_encoding(hidden_state, no_encoding, initial_hidden_state) without return types