class_docstr: ADD Model
  
  Parameters
  ----------
   lr : float
       learning rate
   d_feat : int
       input dimensions for each time step
   metric : str
       the evaluation metric used in early stop
   optimizer : str
       optimizer name
   GPU : int
       the GPU ID used for training
code: def train_epoch(self, x_train_values, y_train_values, m_train_values):
      self.ADD_model.train()
  
      indices = np.arange(len(x_train_values))
      np.random.shuffle(indices)
  
      cur_step = 1
  
      for i in range(len(indices))[:: self.batch_size]:
          if len(indices) - i < self.batch_size:
              break
          batch = indices[i : i + self.batch_size]
          feature = (
              torch.from_numpy(x_train_values[batch])
              .float()
              .to(self.device)
          )
          label_excess = (
              torch.from_numpy(y_train_values[batch])
              .float()
              .to(self.device)
          )
          label_market = (
              torch.from_numpy(m_train_values[batch])
              .long()
              .to(self.device)
          )
  
          preds = self.ADD_model(feature)
  
          loss = self.loss_fn(
              feature, preds, label_excess, label_market
          )
  
          self.train_optimizer.zero_grad()
          loss.backward()
          torch.nn.utils.clip_grad_value_(
              self.ADD_model.parameters(), 3.0
          )
          self.train_optimizer.step()
          cur_step += 1
  
imports: from __future__ import division
  from __future__ import print_function
  import copy
  import math
  from typing import Text, Union
  import numpy
  import pandas
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from qlib.contrib.model.pytorch_gru import GRUModel
  from qlib.contrib.model.pytorch_lstm import LSTMModel
  from qlib.contrib.model.pytorch_utils import count_parameters
  from qlib.data.dataset import DatasetH
  from qlib.data.dataset.handler import DataHandlerLP
  from qlib.log import get_module_logger
  from qlib.model.base import Model
  from qlib.utils import get_or_create_path
  from torch.autograd import Function
label_desc: Use image node_id 14 for calling the ADD obj's underlying member method code with example usage: obj.train_epoch(x_train_values, y_train_values, m_train_values) without return types