class_docstr: TimeSequence feature engineering
docstr: Check dataframe for integrity. Requires time sequence to come in uniform sampling intervals.
  :param input_df:
  :return:
docstr_tok: ["Check","dataframe","for","integrity",".","Requires","time","sequence","to","come","in","uniform","sampling","intervals",".",":","param","input_df",":",":","return",":"]
code: def _check_input(self, input_df, mode="train"):
      """
      Check dataframe for integrity. Requires time sequence to come in uniform sampling intervals.
      :param input_df:
      :return:
      """
      # check NaT in datetime
      input_df = input_df.reset_index()
      dt = input_df[self.dt_col]
      if not np.issubdtype(dt, np.datetime64):
          raise ValueError(
              "The dtype of datetime column is required to be np.datetime64!"
          )
      is_nat = pd.isna(dt)
      if is_nat.any(axis=None):
          raise ValueError("Missing datetime in input dataframe!")
  
      # check uniform (is that necessary?)
      interval = dt[1] - dt[0]
  
      if not all(
          [dt[i] - dt[i - 1] == interval for i in range(1, len(dt))]
      ):
          raise ValueError(
              "Input time sequence intervals are not uniform!"
          )
  
      # check missing values
      if not self.drop_missing:
          is_nan = pd.isna(input_df)
          if is_nan.any(axis=None):
              raise ValueError("Missing values in input dataframe!")
  
      # check if the length of input data is smaller than requested.
      if mode == "test":
          min_input_len = self.past_seq_len
          error_msg = (
              "Length of {} data should be larger than "
              "the past sequence length selected by automl.\n"
              "{} data length: {}\n"
              "past sequence length selected: {}\n".format(
                  mode, mode, len(input_df), self.past_seq_len
              )
          )
      else:
          min_input_len = self.past_seq_len + self.future_seq_len
          error_msg = (
              "Length of {} data should be larger than "
              "the sequence length you want to predict "
              "plus the past sequence length selected by automl.\n"
              "{} data length: {}\n"
              "predict sequence length: {}\n"
              "past sequence length selected: {}\n".format(
                  mode,
                  mode,
                  len(input_df),
                  self.future_seq_len,
                  self.past_seq_len,
              )
          )
      if len(input_df) < min_input_len:
          raise ValueError(error_msg)
  
      return input_df
  
imports: from zoo.chronos.autots.deprecated.feature.utils import save_config
  from zoo.chronos.autots.deprecated.feature.abstract import BaseFeatureTransformer
  from zoo.chronos.utils import deprecated
  import sklearn
  from sklearn.preprocessing import StandardScaler
  import pandas
  import numpy
  import json
  from packaging import version
label_desc: Use image node_id 16 for calling the TimeSequenceFeatureTransformer obj's underlying member method code with example usage: obj._check_input(input_df, mode) and returns: input_df