code: def __init__(
      self,
      context_window_size,
      forecast_horizon=1,
      stratify_targets=False,
      n_ignore_last=0,
      num_samples=-1,
      use_feat_static_real=False,
      use_past_feat_dynamic_real=False,
      use_feat_dynamic_real=False,
      use_feat_dynamic_cat=False,
      cardinality: Cardinality = CardinalityLabel.auto,
      # Should improve accuracy but will slow down model
      one_hot_encode: bool = True,
      subtract_mean: bool = True,
      count_nans: bool = False,
      **kwargs,
  ):
      if one_hot_encode:
          assert cardinality != "ignore" or (
              isinstance(cardinality, List)
              and all(c > 0 for c in cardinality)
          ), (
              "You should set `one_hot_encode=True` if and only if"
              " cardinality is a valid list or not ignored: {}"
          )
  
      super().__init__(
          context_window_size=context_window_size,
          forecast_horizon=forecast_horizon,
          stratify_targets=stratify_targets,
          n_ignore_last=n_ignore_last,
          num_samples=num_samples,
          **kwargs,
      )
  
      self.use_feat_static_real = use_feat_static_real
      self.cardinality = cardinality
      self.use_past_feat_dynamic_real = use_past_feat_dynamic_real
      self.use_feat_dynamic_real = use_feat_dynamic_real
      self.use_feat_dynamic_cat = use_feat_dynamic_cat
      self.one_hot_encode = one_hot_encode
      self.subtract_mean = subtract_mean
      self.count_nans = count_nans
      time_series_window, stats_dict = self._pre_transform(
          [0] * self.context_window_size, subtract_mean, count_nans
      )
      self.dynamic_length = len(time_series_window) + len(
          stats_dict.values()
      )
  
      self.num_feat_dynamic_cat = None
      self.num_feat_dynamic_real = None
      self.num_feat_static_cat = None
      self.num_feat_static_real = None
      self.num_past_feat_dynamic_real = None
  
imports: import concurrent.futures
  import logging
  from enum import Enum
  from itertools import chain, starmap
  from typing import Dict, List, Tuple, Union, Optional, Sequence
  import numpy
  from gluonts.core.component import validated
label_desc: Use image node_id 1 to create a new PreprocessOnlyLagFeatures object from inherited base classes: PreprocessGeneric with example: obj = PreprocessOnlyLagFeatures(context_window_size, forecast_horizon, stratify_targets, n_ignore_last, num_samples, use_feat_static_real, use_past_feat_dynamic_real, use_feat_dynamic_real, use_feat_dynamic_cat, cardinality, one_hot_encode, subtract_mean, count_nans)