code: def _build_model(self):
      try:
          klass = getattr(nn, self.rnn_type.upper())
      except Exception as e:
          raise ValueError(
              "unknown rnn_type `%s`" % self.rnn_type
          ) from e
      self.net = nn.Sequential()
      self.net.add_module(
          "fc_in",
          nn.Linear(
              in_features=self.input_size, out_features=self.hid_size
          ),
      )
      self.net.add_module("act", nn.Tanh())
      self.rnn = klass(
          input_size=self.hid_size,
          hidden_size=self.hid_size,
          num_layers=self.rnn_layer,
          batch_first=True,
          dropout=self.dropout,
      )
      self.fc_out = nn.Linear(
          in_features=self.hid_size * 2, out_features=1
      )
      self.att_net = nn.Sequential()
      self.att_net.add_module(
          "att_fc_in",
          nn.Linear(
              in_features=self.hid_size,
              out_features=int(self.hid_size / 2),
          ),
      )
      self.att_net.add_module(
          "att_dropout", torch.nn.Dropout(self.dropout)
      )
      self.att_net.add_module("att_act", nn.Tanh())
      self.att_net.add_module(
          "att_fc_out",
          nn.Linear(
              in_features=int(self.hid_size / 2),
              out_features=1,
              bias=False,
          ),
      )
      self.att_net.add_module("att_softmax", nn.Softmax(dim=1))
  
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 .pytorch_utils import count_parameters
  from ...model.base import Model
  from ...data.dataset import DatasetH
  from ...data.dataset.handler import DataHandlerLP
label_desc: Use image node_id 2 for calling the ALSTMModel obj's underlying member method code with example usage: obj._build_model() without return types