code: def _check_consistency(
      self, instances: List[TimeSeries]
  ) -> List[TimeSeries]:
      def _consistent(ts1: TimeSeries, ts2: TimeSeries) -> bool:
          return all(
              [
                  np.array_equal(ts1.target_names, ts2.target_names),
                  ts1._static_features == ts2._static_features,
                  ts1._revealed_features == ts2._revealed_features,
                  ts1._observed_features == ts2._observed_features,
                  ts1._categorical_features
                  == ts2._categorical_features,
                  ts1._numerical_features == ts2._numerical_features,
              ]
          )
  
      cats = defaultdict(list)
      nums = defaultdict(list)
      ref = instances[0]
      for i, ts in enumerate(instances):
          if not _consistent(ref, ts):
              raise ValueError("Given time series are not consistent")
          for name in ts._categorical_features:
              cats[name].append(ts._features[name])
          for name in ts._numerical_features & ts._static_features:
              nums[name].append(ts._features[name])
      for name, vals in cats.items():
          data = np.stack(vals, axis=0).flatten()
          self.categorical_encoders[name].fit(data)
      for name, vals in nums.items():
          data = np.stack(vals, axis=0)
          self.numerical_scalers[name].fit(data)
      return instances
  
imports: from __future__ import annotations
  from typing import List, Tuple, Dict, Union, Sequence, Optional, Callable, overload
  from operator import eq
  from collections import defaultdict
  import warnings
  from sklearn.preprocessing import LabelEncoder, StandardScaler
  from numpy import ndarray
  from tqdm import tqdm
  import numpy
  import pandas
  import torch
label_desc: Use image node_id 2 for calling the TimeSeriesCorpus obj's underlying member method code with example usage: obj._check_consistency(instances) and returns: instances, all