code: def build_value_network(
      self, state_normalization_data: NormalizationData
  ) -> torch.nn.Module:
      state_dim = get_num_output_features(
          state_normalization_data.dense_normalization_parameters
      )
  
      return Seq2RewardNetwork(
          state_dim=state_dim,
          action_dim=self.action_dim,
          num_hiddens=self.num_hiddens,
          num_hidden_layers=self.num_hidden_layers,
      )
  
file_path: ReAgent/reagent/net_builder/value/seq2reward_rnn.py
imports: import torch
  from reagent.core.dataclasses import dataclass
  from reagent.core.parameters import NormalizationData, param_hash
  from reagent.models.seq2reward_model import Seq2RewardNetwork
  from reagent.net_builder.value_net_builder import ValueNetBuilder
  from reagent.preprocessing.normalization import get_num_output_features
label_desc: Use image node_id 1 for calling the Seq2RewardNetBuilder obj's underlying member method code with example usage: obj.build_value_network(state_normalization_data) and returns: Seq2RewardNetwork