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.
docstr: Return AnalogSignal object.
docstr_tok: ["Return","AnalogSignal","object","."]
code: def load(self, **kwargs):
      """Return AnalogSignal object."""
      _soft_import("neo", "Reading NeuralynxIO files", strict=True)
      from neo import AnalogSignal
  
      # `kwargs` is a dummy argument to mirror the
      # AnalogSignalProxy.load() call signature which
      # accepts `channel_indexes`` argument; but here we don't need
      # any extra data selection arguments since
      # self.signal array is already in the correct shape
      # (channel dimension is based on `idx` variable)
  
      sig = AnalogSignal(
          signal=self.signal,
          units=self.units,
          sampling_rate=self.sampling_rate,
      )
      return sig
  
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 2 for calling the AnalogSignalGap obj's underlying member method code with example usage: obj.load() and returns: sig