class_docstr: An Elman RNN cell with tanh or ReLU non-linearity.
code: def __init__(
      self, input_size, hidden_size, bias=True, nonlinearity="tanh"
  ):
      super(QuantRNNCell, self).__init__()
      self.input_size = input_size
      self.hidden_size = hidden_size
      self.bias = bias
      self.nonlinearity = nonlinearity
      self.weight_ih = Parameter(torch.Tensor(hidden_size, input_size))
      self.weight_hh = Parameter(torch.Tensor(hidden_size, hidden_size))
      if bias:
          self.bias_ih = Parameter(torch.Tensor(hidden_size))
          self.bias_hh = Parameter(torch.Tensor(hidden_size))
      else:
          self.register_parameter("bias_ih", None)
          self.register_parameter("bias_hh", None)
      self.reset_parameters()
  
imports: import math
  import torch
  import warnings
  import itertools
  import numbers
  from torch import nn
  from torch.nn import Parameter
  from torch.nn.utils.rnn import PackedSequence
  from csrc.pytorch.pytorch_quantization import tensor_quant
  from csrc.pytorch.pytorch_quantization.nn._functions import quant_rnn
  from .None import _utils
label_desc: Use image node_id 1 to create a new QuantRNNCell object from inherited base classes: QuantRNNCellBase with example: obj = QuantRNNCell(input_size, hidden_size, bias, nonlinearity)