code: def forward(
      self,
      input_ids,
      attention_mask,
      output_attentions=False,
      output_hidden_states=False,
      return_dict=True,
  ):
      decoder_cells = None
      embeddings = self.word_embeddings_encoder(input_ids)
      X = torch.nn.utils.rnn.pack_padded_sequence(
          embeddings,
          attention_mask.sum(dim=1).tolist(),
          batch_first=True,
          enforce_sorted=False,
      )
      if self.model_name != "lstm":
          # rnn,gru
          h_0, _ = self.init_hidden(attention_mask.shape[0])
          encoder_outputs, encoder_last_hidden_states = self.encoder(
              X, h_0
          )
      else:
          # lstm
          h_0, c_0 = self.init_hidden(attention_mask.shape[0])
          encoder_outputs, (
              encoder_last_hidden_states,
              encoder_last_cells,
          ) = self.encoder(X, (h_0, c_0))
      encoder_outputs, _ = torch.nn.utils.rnn.pad_packed_sequence(
          encoder_outputs
      )
      encoder_outputs = encoder_outputs.transpose(0, 1)
      decoder_hidden_states = (
          encoder_last_hidden_states.transpose(0, 1)
          .reshape(attention_mask.shape[0], self.num_layers, -1)
          .transpose(0, 1)
      )
      decoder_hidden_states = self.h_projection(decoder_hidden_states)
      decoder_hidden_states = [t for t in decoder_hidden_states]
      if self.model_name == "lstm":
          encoder_cells = (
              encoder_last_cells.transpose(0, 1)
              .reshape(attention_mask.shape[0], self.num_layers, -1)
              .transpose(0, 1)
          )
  
          decoder_cells = self.c_projection(encoder_cells)
          decoder_cells = [t for t in decoder_cells]
      return RNNOutput(
          decoder_hidden_states_before=decoder_hidden_states,
          encoder_last_hidden_state=encoder_outputs,
          last_hidden_state=encoder_outputs,
          decoder_hidden_state_last_layer=decoder_hidden_states[-1],
          decoder_cells_before=decoder_cells,
      )
  
imports: import inspect
  import warnings
  from typing import Optional, Tuple, Dict, Any
  import os
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers.utils import ModelOutput
  from transformers.modeling_utils import PreTrainedModel
  from transformers.configuration_utils import PretrainedConfig
  from torch.nn import CrossEntropyLoss
  from .abstract_model import AbstractModel
label_desc: Use image node_id 3 for calling the RNNEncoder obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, output_attentions, output_hidden_states, return_dict) and returns: RNNOutput