code: def __init__(self, input_size, hidden_size, dropout=0.0):
      super(LSTMCell, self).__init__()
      self.input_size = input_size
      self.hidden_size = hidden_size
      self.weight_ih = nn.Parameter(
          torch.randn(4 * hidden_size, input_size)
      )
      self.weight_hh = nn.Parameter(
          torch.randn(4 * hidden_size, hidden_size)
      )
      self.bias_ih = nn.Parameter(torch.randn(4 * hidden_size))
      self.bias_hh = nn.Parameter(torch.randn(4 * hidden_size))
      self.dropout = dropout
  
imports: from typing import List, Optional
  import torch
  import torch.nn
  from ..losses.pytorch import MAE
  from ..common._base_recurrent import BaseRecurrent
  from ..common._modules import MLP
label_desc: Use image node_id 1 to create a new LSTMCell object from inherited base classes: nn with example: obj = LSTMCell(input_size, hidden_size, dropout)