code: def insert_missing_vals_middle(
      self, ts_len: int, constant: Optional[float]
  ) -> List[Optional[float]]:
      target: List[Optional[float]] = []
      lower_bound = (
          self.num_training_steps - self.num_missing_middle
      ) // 2
      upper_bound = (
          self.num_training_steps + self.num_missing_middle
      ) // 2
      num_missing_endpts = math.floor(0.1 * self.num_missing_middle)
      for j in range(ts_len):
          if (
              0 < j < lower_bound
              and j % (2 * num_missing_endpts) == 0
              or lower_bound <= j < upper_bound
              or j >= upper_bound
              and j % (2 * num_missing_endpts) == 0
          ):
              target.append(float("nan"))
          else:
              target.append(constant)
  
      return target
  
imports: import math
  import random
  from abc import abstractmethod
  from typing import Callable, Dict, List, NamedTuple, Optional, Tuple, Union, cast
  import numpy
  import pandas
  from pandas.tseries.frequencies import to_offset
  from pandas.tseries.offsets import BaseOffset, Week
  from gluonts.dataset.artificial.recipe import BinaryHolidays, BinaryMarkovChain, Constant, ForEachCat, Lag, LinearTrend, RandomCat, RandomGaussian, Stack, generate, take_as_list
  from gluonts.dataset.common import BasicFeatureInfo, CategoricalFeatureInfo, DataEntry, Dataset, ListDataset, MetaData, TrainDatasets
  from gluonts.dataset.field_names import FieldName
  from gluonts.dataset.stat import DatasetStatistics, calculate_dataset_statistics
label_desc: Use image node_id 8 for calling the ConstantDataset obj's underlying member method code with example usage: obj.insert_missing_vals_middle(ts_len, constant) and returns: target