class_docstr: A long short-term memory (LSTM) cell.
code: def __init__(self, input_size, hidden_size, bias=True, **kwargs):
      super(QuantLSTMCell, self).__init__()
      self.input_size = input_size
      self.hidden_size = hidden_size
      self.bias = bias
      self.weight_ih = Parameter(
          torch.Tensor(4 * hidden_size, input_size)
      )
      self.weight_hh = Parameter(
          torch.Tensor(4 * hidden_size, hidden_size)
      )
      if bias:
          self.bias_ih = Parameter(torch.Tensor(4 * hidden_size))
          self.bias_hh = Parameter(torch.Tensor(4 * hidden_size))
      else:
          self.register_parameter("bias_ih", None)
          self.register_parameter("bias_hh", None)
      self.reset_parameters()
      (
          quant_desc_input,
          quant_desc_weight,
      ) = _utils.pop_quant_desc_in_kwargs(self.__class__, **kwargs)
      self.init_quantizer(quant_desc_input, quant_desc_weight)
  
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 lightseq.training.pytorch_quantization import tensor_quant
  from lightseq.training.pytorch_quantization.nn._functions import quant_rnn
  from .None import _utils
label_desc: Use image node_id 1 to create a new QuantLSTMCell object from inherited base classes: QuantRNNCellBase with example: obj = QuantLSTMCell(input_size, hidden_size, bias)