code: def forward(self, weighted_attn):
      # Get LSTM state
      state = registry.get(f"{weighted_attn.device}_lstm_state")
      h1, c1 = state["td_hidden"]
      h2, c2 = state["lm_hidden"]
  
      # Language LSTM
      h2, c2 = self.language_lstm(
          torch.cat([weighted_attn, h1], dim=1), (h2, c2)
      )
      predictions = self.fc(self.dropout(h2))
  
      # Update hidden state for t+1
      state["lm_hidden"] = (h2, c2)
  
      return predictions
  
file_path: mmf/mmf/modules/decoders.py
imports: import torch
  from mmf.common.registry import registry
  from torch import nn
  from torch.nn.utils.weight_norm import weight_norm
label_desc: Use image node_id 3 for calling the LanguageDecoder obj's underlying member method code with example usage: obj.forward(weighted_attn) and returns: predictions