code: def get_initial_hidden_state(self, state, batch_size: int = 1):
      # state embedding with linear mapping
      # repeat state to fill num_hidden_layers at first dimension
      state = state.repeat(self.num_hidden_layers, 1, 1)
      state_embed = self.map_linear(state)
  
      # hidden = (hidden,cell) where hidden is init with liner map
      # of input state and cell is 0.
      # hidden :
      # TUPLE(
      #     (NUM_LAYERS, BATCH_SIZE, HIDDEN_SIZE),
      #     (NUM_LAYERS, BATCH_SIZE, HIDDEN_SIZE)
      # ) torch tensor
      hidden = (
          state_embed,
          torch.zeros(
              self.num_hidden_layers, batch_size, self.num_hiddens
          ).to(state.device),
      )
  
      return hidden
  
file_path: ReAgent/reagent/models/seq2reward_model.py
imports: from typing import Optional
  import torch
  import torch.nn
  from reagent.core import types
  from reagent.models.base import ModelBase
label_desc: Use image node_id 4 for calling the Seq2RewardNetwork obj's underlying member method code with example usage: obj.get_initial_hidden_state(state, batch_size) and returns: hidden