class_docstr: This base class provides basic operations to consume inputs and call a synthetic reward net
  
  A synthetic reward net (self.net) assumes the input contains only torch.Tensors.
  Expected input shape:
      state: seq_len, batch_size, state_dim
      action: seq_len, batch_size, action_dim
  Expected output shape:
      reward: batch_size, seq_len
code: def __init__(self, net: nn.Module):
      super().__init__()
      self.net = net
  
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 1 to create a new SyntheticRewardSparseArchNet object from inherited base classes: ModelBase with example: obj = SyntheticRewardSparseArchNet(net)