code: def forward(self, state: torch.Tensor, action: torch.Tensor):
      # shape: seq_len (L), batch_size (B), state_dim + action_dim
      cat_input = torch.cat((state, action), dim=-1)
      # latent_input shape: (L,B,E)
      latent_input = self.fc_in(cat_input)
      # output shape: (L, B, E)
      output = self.transformer(latent_input)
      output = self.fc_out(output)
      # output shape: seq_len, batch_size, 1
      output = self.output_activation(output).squeeze(2).transpose(0, 1)
      # output shape: batch_size, seq_len
      return output
  
imports: import logging
  import math
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from reagent.core import parameters, types
  from reagent.models import convolutional_network, fully_connected_network
  from reagent.models.base import ModelBase
  from reagent.models.fully_connected_network import ACTIVATION_MAP
label_desc: Use image node_id 2 for calling the TransformerSyntheticRewardNet obj's underlying member method code with example usage: obj.forward(state, action) and returns: output