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 __init__(
      self,
      input_size,
      num_states=1,
      hidden_size=8,
      tau=1.0,
      src_info="LR_TPE",
  ):
      super().__init__()
  
      self.num_states = num_states
      self.tau = tau
      self.src_info = src_info
  
      if num_states > 1:
          self.router = nn.LSTM(
              input_size=num_states,
              hidden_size=hidden_size,
              num_layers=1,
              batch_first=True,
          )
          self.fc = nn.Linear(hidden_size + input_size, num_states)
  
      self.predictors = nn.Linear(input_size, num_states)
  
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 1 to create a new TRA object from inherited base classes: nn with example: obj = TRA(input_size, num_states, hidden_size, tau, src_info)