code: def train_epoch(self, data_set):
      self.model.train()
      self.tra.train()
  
      data_set.train()
  
      max_steps = self.n_epochs
      if self.max_steps_per_epoch is not None:
          max_steps = min(self.max_steps_per_epoch, self.n_epochs)
  
      count = 0
      total_loss = 0
      total_count = 0
      for batch in tqdm(data_set, total=max_steps):
          count += 1
          if count > max_steps:
              break
  
          self.global_step += 1
  
          data, label, index = (
              batch["data"],
              batch["label"],
              batch["index"],
          )
  
          feature = data[:, :, : -self.tra.num_states]
          hist_loss = data[
              :, : -data_set.horizon, -self.tra.num_states :
          ]
  
          hidden = self.model(feature)
          pred, all_preds, prob = self.tra(hidden, hist_loss)
  
          loss = (pred - label).pow(2).mean()
  
          L = (all_preds.detach() - label[:, None]).pow(2)
          L -= L.min(
              dim=-1, keepdim=True
          ).values  # normalize & ensure positive input
  
          data_set.assign_data(index, L)  # save loss to memory
  
          if prob is not None:
              P = sinkhorn(-L, epsilon=0.01)  # sample assignment matrix
              lamb = self.lamb * (self.rho**self.global_step)
              reg = prob.log().mul(P).sum(dim=-1).mean()
              loss = loss - lamb * reg
  
          loss.backward()
          self.optimizer.step()
          self.optimizer.zero_grad()
  
          total_loss += loss.item()
          total_count += len(pred)
  
      total_loss /= total_count
  
      return total_loss
  
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 TRAModel obj's underlying member method code with example usage: obj.train_epoch(data_set) and returns: total_loss