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 test_epoch(self, data_x, data_y, data_m):
      x_values = data_x.values
      y_values = np.squeeze(data_y.values)
      m_values = np.squeeze(data_m.values.astype(int))
      self.ADD_model.eval()
  
      metrics_list = []
  
      daily_index, daily_count = self.get_daily_inter(
          data_x, shuffle=False
      )
  
      for idx, count in zip(daily_index, daily_count):
          batch = slice(idx, idx + count)
          feature = (
              torch.from_numpy(x_values[batch]).float().to(self.device)
          )
          label_excess = (
              torch.from_numpy(y_values[batch]).float().to(self.device)
          )
          label_market = (
              torch.from_numpy(m_values[batch]).long().to(self.device)
          )
  
          metrics = {}
          preds = self.ADD_model(feature)
          self.loss_fn(
              feature, preds, label_excess, label_market, metrics
          )
          metrics.update(
              self.cal_ic_metrics(preds["excess"], label_excess)
          )
          metrics_list.append(metrics)
      metrics = {}
      keys = metrics_list[0].keys()
      for k in keys:
          vs = [m[k] for m in metrics_list]
          metrics[k] = sum(vs) / len(vs)
  
      return metrics
  
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 13 for calling the ADD obj's underlying member method code with example usage: obj.test_epoch(data_x, data_y, data_m) and returns: metrics