class_docstr: Temporal Routing Adaptor (TRA)
  
  TRA takes historical prediction errors & latent representation as inputs,
  then routes the input sample to a specific predictor for training & inference.
  
  Args:
      input_size (int): input size (RNN/Transformer's hidden size)
      num_states (int): number of latent states (i.e., trading patterns)
          If `num_states=1`, then TRA falls back to traditional methods
      hidden_size (int): hidden size of the router
      tau (float): gumbel softmax temperature
code: def forward(self, hidden, hist_loss):
      preds = self.predictors(hidden)
  
      if self.num_states == 1:
          return preds.squeeze(-1), preds, None
  
      # information type
      router_out, _ = self.router(hist_loss)
      if "LR" in self.src_info:
          latent_representation = hidden
      else:
          latent_representation = torch.randn(hidden.shape).to(hidden)
      if "TPE" in self.src_info:
          temporal_pred_error = router_out[:, -1]
      else:
          temporal_pred_error = torch.randn(router_out[:, -1].shape).to(
              hidden
          )
  
      out = self.fc(
          torch.cat(
              [temporal_pred_error, latent_representation], dim=-1
          )
      )
      prob = F.gumbel_softmax(out, dim=-1, tau=self.tau, hard=False)
  
      if self.training:
          final_pred = (preds * prob).sum(dim=-1)
      else:
          final_pred = preds[range(len(preds)), prob.argmax(dim=-1)]
  
      return final_pred, preds, prob
  
imports: import os
  import copy
  import math
  import json
  import collections
  import numpy
  import pandas
  import torch
  import torch.nn
  import torch.optim
  import torch.nn.functional
  from tqdm import tqdm
  from qlib.utils import get_or_create_path
  from qlib.log import get_module_logger
  from qlib.model.base import Model
label_desc: Use image node_id 2 for calling the TRA obj's underlying member method code with example usage: obj.forward(hidden, hist_loss) and returns: final_pred,  preds,  prob, preds,  preds,  None