class_docstr: HIST 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 : str
      the GPU ID(s) used for training
code: def test_epoch(self, data_x, data_y, stock_index):
      # prepare training data
      stock2concept_matrix = np.load(self.stock2concept)
      x_values = data_x.values
      y_values = np.squeeze(data_y.values)
      stock_index = stock_index.values
      stock_index[np.isnan(stock_index)] = 733
      self.HIST_model.eval()
  
      scores = []
      losses = []
  
      # organize the test data into daily batches
      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)
          )
          concept_matrix = (
              torch.from_numpy(stock2concept_matrix[stock_index[batch]])
              .float()
              .to(self.device)
          )
          label = (
              torch.from_numpy(y_values[batch]).float().to(self.device)
          )
          with torch.no_grad():
              pred = self.HIST_model(feature, concept_matrix)
              loss = self.loss_fn(pred, label)
              losses.append(loss.item())
  
              score = self.metric_fn(pred, label)
              scores.append(score.item())
  
      return np.mean(losses), np.mean(scores)
  
imports: from __future__ import division
  from __future__ import print_function
  import os
  import numpy
  import pandas
  from typing import Text, Union
  import urllib.request
  import copy
  from ...utils import get_or_create_path
  from ...log import get_module_logger
  import torch
  import torch.nn
  import torch.optim
  from .pytorch_utils import count_parameters
  from ...model.base import Model
  from ...data.dataset import DatasetH
  from ...data.dataset.handler import DataHandlerLP
  from ...contrib.model.pytorch_lstm import LSTMModel
  from ...contrib.model.pytorch_gru import GRUModel
label_desc: Use image node_id 8 for calling the HIST obj's underlying member method code with example usage: obj.test_epoch(data_x, data_y, stock_index) and returns: np,  np