code: def generate_trade_decision(self, execute_result=None):
      # get the number of trading step finished, trade_step can be [0, 1, 2, ..., trade_len - 1]
      trade_step = self.trade_calendar.get_trade_step()
      (
          trade_start_time,
          trade_end_time,
      ) = self.trade_calendar.get_step_time(trade_step)
      (
          pred_start_time,
          pred_end_time,
      ) = self.trade_calendar.get_step_time(trade_step, shift=1)
      pred_score = self.signal.get_signal(
          start_time=pred_start_time, end_time=pred_end_time
      )
      # NOTE: the current version of topk dropout strategy can't handle pd.DataFrame(multiple signal)
      # So it only leverage the first col of signal
      if isinstance(pred_score, pd.DataFrame):
          pred_score = pred_score.iloc[:, 0]
      if pred_score is None:
          return TradeDecisionWO([], self)
      if self.only_tradable:
          # If The strategy only consider tradable stock when make decision
          # It needs following actions to filter stocks
          def get_first_n(li, n, reverse=False):
              cur_n = 0
              res = []
              for si in reversed(li) if reverse else li:
                  if self.trade_exchange.is_stock_tradable(
                      stock_id=si,
                      start_time=trade_start_time,
                      end_time=trade_end_time,
                  ):
                      res.append(si)
                      cur_n += 1
                      if cur_n >= n:
                          break
              return res[::-1] if reverse else res
  
          def get_last_n(li, n):
              return get_first_n(li, n, reverse=True)
  
          def filter_stock(li):
              return [
                  si
                  for si in li
                  if self.trade_exchange.is_stock_tradable(
                      stock_id=si,
                      start_time=trade_start_time,
                      end_time=trade_end_time,
                  )
              ]
  
      else:
          # Otherwise, the stock will make decision without the stock tradable info
          def get_first_n(li, n):
              return list(li)[:n]
  
          def get_last_n(li, n):
              return list(li)[-n:]
  
          def filter_stock(li):
              return li
  
      current_temp: Position = copy.deepcopy(self.trade_position)
      # generate order list for this adjust date
      sell_order_list = []
      buy_order_list = []
      # load score
      cash = current_temp.get_cash()
      current_stock_list = current_temp.get_stock_list()
      # last position (sorted by score)
      last = (
          pred_score.reindex(current_stock_list)
          .sort_values(ascending=False)
          .index
      )
      # The new stocks today want to buy **at most**
      if self.method_buy == "top":
          today = get_first_n(
              pred_score[~pred_score.index.isin(last)]
              .sort_values(ascending=False)
              .index,
              self.n_drop + self.topk - len(last),
          )
      elif self.method_buy == "random":
          topk_candi = get_first_n(
              pred_score.sort_values(ascending=False).index, self.topk
          )
          candi = list(filter(lambda x: x not in last, topk_candi))
          n = self.n_drop + self.topk - len(last)
          try:
              today = np.random.choice(candi, n, replace=False)
          except ValueError:
              today = candi
      else:
          raise NotImplementedError(
              f"This type of input is not supported"
          )
      # combine(new stocks + last stocks),  we will drop stocks from this list
      # In case of dropping higher score stock and buying lower score stock.
      comb = (
          pred_score.reindex(last.union(pd.Index(today)))
          .sort_values(ascending=False)
          .index
      )
  
      # Get the stock list we really want to sell (After filtering the case that we sell high and buy low)
      if self.method_sell == "bottom":
          sell = last[last.isin(get_last_n(comb, self.n_drop))]
      elif self.method_sell == "random":
          candi = filter_stock(last)
          try:
              sell = pd.Index(
                  np.random.choice(candi, self.n_drop, replace=False)
                  if len(last)
                  else []
              )
          except ValueError:  # No enough candidates
              sell = candi
      else:
          raise NotImplementedError(
              f"This type of input is not supported"
          )
  
      # Get the stock list we really want to buy
      buy = today[: len(sell) + self.topk - len(last)]
      for code in current_stock_list:
          if not self.trade_exchange.is_stock_tradable(
              stock_id=code,
              start_time=trade_start_time,
              end_time=trade_end_time,
              direction=None
              if self.forbid_all_trade_at_limit
              else OrderDir.SELL,
          ):
              continue
          if code in sell:
              # check hold limit
              time_per_step = self.trade_calendar.get_freq()
              if (
                  current_temp.get_stock_count(code, bar=time_per_step)
                  < self.hold_thresh
              ):
                  continue
              # sell order
              sell_amount = current_temp.get_stock_amount(code=code)
              # sell_amount = self.trade_exchange.round_amount_by_trade_unit(sell_amount, factor)
              sell_order = Order(
                  stock_id=code,
                  amount=sell_amount,
                  start_time=trade_start_time,
                  end_time=trade_end_time,
                  direction=Order.SELL,  # 0 for sell, 1 for buy
              )
              # is order executable
              if self.trade_exchange.check_order(sell_order):
                  sell_order_list.append(sell_order)
                  (
                      trade_val,
                      trade_cost,
                      trade_price,
                  ) = self.trade_exchange.deal_order(
                      sell_order, position=current_temp
                  )
                  # update cash
                  cash += trade_val - trade_cost
      # buy new stock
      # note the current has been changed
      # current_stock_list = current_temp.get_stock_list()
      value = cash * self.risk_degree / len(buy) if len(buy) > 0 else 0
  
      # open_cost should be considered in the real trading environment, while the backtest in evaluate.py does not
      # consider it as the aim of demo is to accomplish same strategy as evaluate.py, so comment out this line
      # value = value / (1+self.trade_exchange.open_cost) # set open_cost limit
      for code in buy:
          # check is stock suspended
          if not self.trade_exchange.is_stock_tradable(
              stock_id=code,
              start_time=trade_start_time,
              end_time=trade_end_time,
              direction=None
              if self.forbid_all_trade_at_limit
              else OrderDir.BUY,
          ):
              continue
          # buy order
          buy_price = self.trade_exchange.get_deal_price(
              stock_id=code,
              start_time=trade_start_time,
              end_time=trade_end_time,
              direction=OrderDir.BUY,
          )
          buy_amount = value / buy_price
          factor = self.trade_exchange.get_factor(
              stock_id=code,
              start_time=trade_start_time,
              end_time=trade_end_time,
          )
          buy_amount = self.trade_exchange.round_amount_by_trade_unit(
              buy_amount, factor
          )
          buy_order = Order(
              stock_id=code,
              amount=buy_amount,
              start_time=trade_start_time,
              end_time=trade_end_time,
              direction=Order.BUY,  # 1 for buy
          )
          buy_order_list.append(buy_order)
      return TradeDecisionWO(sell_order_list + buy_order_list, self)
  
imports: import os
  import copy
  import warnings
  import numpy
  import pandas
  from typing import Dict, List, Text, Tuple, Union
  from abc import ABC
  from qlib.data import D
  from qlib.data.dataset import Dataset
  from qlib.model.base import BaseModel
  from qlib.strategy.base import BaseStrategy
  from qlib.backtest.position import Position
  from qlib.backtest.signal import Signal, create_signal_from
  from qlib.backtest.decision import Order, OrderDir, TradeDecisionWO
  from qlib.log import get_module_logger
  from qlib.utils import get_pre_trading_date, load_dataset
  from qlib.contrib.strategy.order_generator import OrderGenerator, OrderGenWOInteract
  from qlib.contrib.strategy.optimizer import EnhancedIndexingOptimizer
label_desc: Use image node_id 2 for calling the TopkDropoutStrategy obj's underlying member method code with example usage: obj.generate_trade_decision(execute_result) and returns: TradeDecisionWO, TradeDecisionWO, unknown, get_first_n, unknown, list, list, li