class_docstr: IGMTF Model
  
  Parameters
  ----------
  d_feat : int
      input dimension 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, train_hidden, train_hidden_day):
      # prepare training data
      x_values = data_x.values
      y_values = np.squeeze(data_y.values)
  
      self.igmtf_model.eval()
  
      scores = []
      losses = []
  
      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 = (
              torch.from_numpy(y_values[batch]).float().to(self.device)
          )
  
          pred = self.igmtf_model(
              feature,
              train_hidden=train_hidden,
              train_hidden_day=train_hidden_day,
          )
          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 numpy
  import pandas
  from typing import Text, Union
  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 9 for calling the IGMTF obj's underlying member method code with example usage: obj.test_epoch(data_x, data_y, train_hidden, train_hidden_day) and returns: np,  np