class_docstr: A gated recurrent unit (GRU) cell
  
  DP-friendly drop-in replacement of the ``torch.nn.GRUCell`` module to use in ``DPGRU``.
  Refer to ``torch.nn.GRUCell`` documentation for the model description, parameters and inputs/outputs.
class_docstr_tok: ["A","gated","recurrent","unit","(","GRU",")","cell","DP-friendly","drop-in","replacement","of","the","``","torch.nn.GRUCell","``","module","to","use","in","``","DPGRU","``",".","Refer","to","``","torch.nn.GRUCell","``","documentation","for","the","model","description",",","parameters","and","inputs\/outputs","."]
code: def __init__(
      self, input_size: int, hidden_size: int, bias: bool
  ) -> None:
      super().__init__(input_size, hidden_size, bias, num_chunks=3)
  
imports: import math
  import numbers
  import warnings
  from typing import List, Optional, Tuple, Type, Union
  import torch
  import torch.nn
  from torch import Tensor
  from torch.nn.utils.rnn import PackedSequence
  from ..utils.packed_sequences import compute_seq_lengths
  from .param_rename import RenameParamsMixin
label_desc: Use image node_id 1 to create a new DPGRUCell object from inherited base classes: DPRNNCellBase with example: obj = DPGRUCell(input_size, hidden_size, bias)