code: def forward(self, x):
      # Gathers batch size
      batch_size = x.size()[1]
  
      # Variable to hold hidden state
      h0 = Variable(
          torch.zeros([1, batch_size, self.n_hidden]),
          requires_grad=False,
      )
  
      # Variable to hold cell state
      c0 = Variable(
          torch.zeros([1, batch_size, self.n_hidden]),
          requires_grad=False,
      )
  
      # Performs forward pass
      fx, _ = self.lstm.forward(x, (h0, c0))
  
      return self.linear.forward(fx[-1])
  
file_path: opytimizer/examples/integrations/pytorch/lstm.py
imports: import numpy
  import torch
  from sklearn.datasets import load_digits
  from sklearn.model_selection import train_test_split
  from torch import optim
  from torch.autograd import Variable
  from opytimizer import Opytimizer
  from opytimizer.core import Function
  from opytimizer.optimizers.swarm import PSO
  from opytimizer.spaces import SearchSpace
label_desc: Use image node_id 2 for calling the LSTM obj's underlying member method code with example usage: obj.forward(x) and returns: self