code: def forward(
      self,
      state: torch.Tensor,
      action: torch.Tensor,
      state_id_list: KeyedJaggedTensor,
      state_id_score_list: KeyedJaggedTensor,
      action_id_list: KeyedJaggedTensor,
      action_id_score_list: KeyedJaggedTensor,
  ):
      # state shape: seq_len, batch_size, state_dim
      # action shape: seq_len, batch_size, action_dim
      # state_sparse: sparse state features from seq_len steps
      seq_len, batch_size, _ = state.shape
  
      # state_dense_out shape: seq_len, batch_size, embed_dim
      state_dense_out = self.state_dense_arch(state)
      # action_dense_out shape: seq_len, batch_size, embed_dim
      action_dense_out = self.action_dense_arch(action)
  
      sparse_data_per_step: List[
          KeyedJaggedTensor
      ] = self.create_sparse_data_per_step(
          state_id_list,
          state_id_score_list,
          action_id_list,
          action_id_score_list,
          seq_len,
      )
      sparse_embed_per_step = [
          self.sparse_arch(sparse_data_per_step[i])
          for i in range(seq_len)
      ]
  
      interaction_per_step = []
      for i in range(seq_len):
          # shape: batch_size, D + F + F choose 2
          inter_sparse_state = self.inter_arch_sparse_and_state_dense(
              dense_features=state_dense_out[i],
              sparse_features=sparse_embed_per_step[i],
          )
          # shape: batch_size, D + F + F choose 2
          inter_sparse_action = self.inter_arch_sparse_and_action_dense(
              dense_features=action_dense_out[i],
              sparse_features=sparse_embed_per_step[i],
          )
          # We need to concat interactions of sparse-state and sparse-action
          # However, sparse feature embeddings' self dot-products are included
          # in both interactions so we need to dedup
          # interaction shape: batch_size, 2D + 2F + F choose 2
          interaction = torch.cat(
              (
                  inter_sparse_state,
                  inter_sparse_action[:, : self.D + self.F],
              ),
              dim=1,
          )
          interaction_per_step.append(interaction)
  
      # interaction_per_step shape: seq_len, batch_size, 2D + 2F + F choose 2
      interaction_per_step = torch.stack(interaction_per_step, dim=0)
      # overall_arch_out shape: seq_len, batch_size, 1
      overall_arch_out = self.overall_arch(interaction_per_step)
      # return shape: batch_size, seq_len
      return overall_arch_out.squeeze(2).transpose(0, 1)
  
imports: import logging
  from typing import List
  import torch
  import torch.nn
  from reagent.core import types
  from reagent.core.torch_utils import split_sequence_keyed_jagged_tensor
  from reagent.models.base import ModelBase
  from reagent.models.fully_connected_network import ACTIVATION_MAP
  from reagent.models.synthetic_reward import _gen_mask
  from torchrec import EmbeddingBagCollection
  from torchrec.models.dlrm import InteractionArch, SparseArch
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
label_desc: Use image node_id 3 for calling the SingleStepSyntheticSparseArchRewardNet obj's underlying member method code with example usage: obj.forward(state, action, state_id_list, state_id_score_list, action_id_list, action_id_score_list) and returns: overall_arch_out