code: def __init__(
      self,
      input_size: int,
      hidden_size: int,
      bias: bool,
      num_chunks: int,
  ) -> None:
      super().__init__()
      self.input_size = input_size
      self.hidden_size = hidden_size
      self.bias = bias
  
      self.ih = RNNLinear(input_size, num_chunks * hidden_size, bias)
      self.hh = RNNLinear(hidden_size, num_chunks * hidden_size, bias)
  
      self.reset_parameters()
  
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 DPRNNCellBase object from inherited base classes: nn with example: obj = DPRNNCellBase(input_size, hidden_size, bias, num_chunks)