code: def forward(self, x):
      # Set initial hidden and cell states
      h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(
          device
      )
      c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(
          device
      )
  
      # Forward propagate LSTM
      out, _ = self.lstm(
          x, (h0, c0)
      )  # out: tensor of shape (batch_size, seq_length, hidden_size)
  
      # Decode the hidden state of the last time step
      out = self.fc(out[:, -1, :])
      return out
  
imports: import torch
  import torch.nn
  import torchvision
  import torchvision.transforms
label_desc: Use image node_id 2 for calling the RNN obj's underlying member method code with example usage: obj.forward(x) and returns: out