code: def forward(self, inputs):
      rnn_out, _ = self.rnn(
          self.net(inputs)
      )  # [batch, seq_len, num_directions * hidden_size]
      attention_score = self.att_net(rnn_out)  # [batch, seq_len, 1]
      out_att = torch.mul(rnn_out, attention_score)
      out_att = torch.sum(out_att, dim=1)
      out = self.fc_out(
          torch.cat((rnn_out[:, -1, :], out_att), dim=1)
      )  # [batch, seq_len, num_directions * hidden_size] -> [batch, 1]
      return out[..., 0]
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  import pandas
  from typing import Text, Union
  import copy
  from ...utils import get_or_create_path
  from ...log import get_module_logger
  import torch
  import torch.nn
  import torch.optim
  from torch.utils.data import DataLoader
  from .pytorch_utils import count_parameters
  from ...model.base import Model
  from ...data.dataset import DatasetH
  from ...data.dataset.handler import DataHandlerLP
  from ...model.utils import ConcatDataset
  from ...data.dataset.weight import Reweighter
label_desc: Use image node_id 3 for calling the ALSTMModel obj's underlying member method code with example usage: obj.forward(inputs) and returns: out