code: def _adjust_times(
      df,
      sfreq,
      time_col="time",
  ):
      """Fill missing timestamps if there are multiple recording blocks.
  
      Parameters
      ----------
      df : pandas.DataFrame:
          dataframe of the eyetracking data samples, BEFORE
          _convert_times() is applied to the dataframe
  
      sfreq : int | float:
          sampling frequency of the data
  
      time_col : str (default 'time'):
          name of column with the timestamps (e.g. 9511881, 9511882, ...)
  
      Returns
      -------
      %(df_return)s
  
      Notes
      -----
      After _parse_recording_blocks, Files with multiple recording blocks will
      have missing timestamps for the duration of the period between the blocks.
      This would cause the occular annotations (i.e. blinks) to not line up with
      the signal.
      """
      pd = _check_pandas_installed()
  
      first, last = df[time_col].iloc[[0, -1]]
      step = 1000 / sfreq
      df[time_col] = df[time_col].astype(float)
      new_times = pd.DataFrame(
          np.arange(first, last + step / 2, step), columns=[time_col]
      )
      return pd.merge_asof(
          new_times,
          df,
          on=time_col,
          direction="nearest",
          tolerance=step / 10,
      )
  
imports: import re
  from datetime import datetime, timedelta, timezone
  import numpy
  from ..._fiff.constants import FIFF
  from ..._fiff.meas_info import create_info
  from ...annotations import Annotations
  from ...utils import _check_pandas_installed, logger, warn
label_desc: Use image node_id 16 for calling a global function with example usage: _adjust_times(df, sfreq, time_col) and returns: pd