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 predict(
      self, dataset: DatasetH, segment: Union[Text, slice] = "test"
  ):
      if not self.fitted:
          raise ValueError("model is not fitted yet!")
  
      stock2concept_matrix = np.load(self.stock2concept)
      stock_index = np.load(self.stock_index, allow_pickle=True).item()
      df_test = dataset.prepare(
          segment, col_set="feature", data_key=DataHandlerLP.DK_I
      )
      df_test["stock_index"] = 733
      df_test["stock_index"] = df_test.index.get_level_values(
          "instrument"
      ).map(stock_index)
      stock_index_test = df_test["stock_index"].values
      stock_index_test[np.isnan(stock_index_test)] = 733
      stock_index_test = stock_index_test.astype("int")
      df_test = df_test.drop(["stock_index"], axis=1)
      index = df_test.index
  
      self.HIST_model.eval()
      x_values = df_test.values
      preds = []
  
      # organize the data into daily batches
      daily_index, daily_count = self.get_daily_inter(
          df_test, shuffle=False
      )
  
      for idx, count in zip(daily_index, daily_count):
          batch = slice(idx, idx + count)
          x_batch = (
              torch.from_numpy(x_values[batch]).float().to(self.device)
          )
          concept_matrix = (
              torch.from_numpy(
                  stock2concept_matrix[stock_index_test[batch]]
              )
              .float()
              .to(self.device)
          )
  
          with torch.no_grad():
              pred = (
                  self.HIST_model(x_batch, concept_matrix)
                  .detach()
                  .cpu()
                  .numpy()
              )
  
          preds.append(pred)
  
      return pd.Series(np.concatenate(preds), index=index)
  
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 10 for calling the HIST obj's underlying member method code with example usage: obj.predict(dataset, segment) and returns: pd