class_docstr: Dummy object to represent gaps in Neuralynx data.
  
  Creates a AnalogSignalProxy-like object.
  Propagate `signal`, `units`, and `sampling_rate` attributes
  to the `AnalogSignal` init returned by `load()`.
  
  Parameters
  ----------
  signal : array-like
      Array of shape (n_samples, n_chans) containing the data.
  units : str
      Units of the data. (e.g., 'uV')
  sampling_rate : quantity
      Sampling rate of the data. (e.g., 4000 * pq.Hz)
  
  Returns
  -------
  sig : instance of AnalogSignal
      A AnalogSignal object representing a gap in Neuralynx data.
code: def __init__(self, signal, units, sampling_rate):
      self.signal = signal
      self.units = units
      self.sampling_rate = sampling_rate
  
file_path: mne-python/mne/io/neuralynx/neuralynx.py
imports: import glob
  import os
  import numpy
  from ..._fiff.meas_info import create_info
  from ..._fiff.utils import _mult_cal_one
  from ...annotations import Annotations
  from ...utils import _check_fname, _soft_import, fill_doc, logger, verbose
  from ..base import BaseRaw
label_desc: Use image node_id 1 to create a new AnalogSignalGap object  with example: obj = AnalogSignalGap(signal, units, sampling_rate)