class_docstr: Constant overlap-add processing helper.
  
  Parameters
  ----------
  process : callable
      A function that takes a chunk of input data with shape
      ``(n_channels, n_samples)`` and processes it.
  store : callable | ndarray
      A function that takes a completed chunk of output data.
      Can also be an ``ndarray``, in which case it is treated as the
      output data in which to store the results.
  n_total : int
      The total number of samples.
  n_samples : int
      The number of samples per window.
  n_overlap : int
      The overlap between windows.
  window : str
      The window to use. Default is "hann".
  tol : float
      The tolerance for COLA checking.
  
  Notes
  -----
  This will process data using overlapping windows to achieve a constant
  output value. For example, for ``n_total=27``, ``n_samples=10``,
  ``n_overlap=5`` and ``window='triang'``::
  
      1 _____               _______
        |    \   /\   /\   /
        |     \ /  \ /  \ /
        |      x    x    x
        |     / \  / \  / \
        |    /   \/   \/   \
      0 +----|----|----|----|----|-
        0    5   10   15   20   25
  
  This produces four windows: the first three are the requested length
  (10 samples) and the last one is longer (12 samples). The first and last
  window are asymmetric.
docstr: Compute from current processing window start and buffer len.
docstr_tok: ["Compute","from","current","processing","window","start","and","buffer","len","."]
code: def _in_offset(self):
      """Compute from current processing window start and buffer len."""
      return self.starts[self._idx] + self._in_buffers[0].shape[-1]
  
file_path: mne-python/mne/_ola.py
imports: import numpy
  from scipy.signal import get_window
  from .utils import _ensure_int, logger, verbose
label_desc: Use image node_id 2 for calling the _COLA obj's underlying member method code with example usage: obj._in_offset() and returns: unknown