docstr: Get a boolean mask indicating the indices where there are gaps.
  
  If you require custom gap handling, you can implement this method to return a
  boolean mask indicating the indices where there are gaps.
  
  Parameters
  ----------
  x_agg: np.ndarray
      The x array. This is used to determine the gaps.
  
  Returns
  -------
  Optional[np.ndarray]
      A boolean mask indicating the indices where there are gaps. If there are no
      gaps, None is returned.
code: def _get_gap_mask(self, x_agg: np.ndarray) -> Optional[np.ndarray]:
      """Get a boolean mask indicating the indices where there are gaps.
  
      If you require custom gap handling, you can implement this method to return a
      boolean mask indicating the indices where there are gaps.
  
      Parameters
      ----------
      x_agg: np.ndarray
          The x array. This is used to determine the gaps.
  
      Returns
      -------
      Optional[np.ndarray]
          A boolean mask indicating the indices where there are gaps. If there are no
          gaps, None is returned.
  
      """
      pass
  
file_path: plotly-resampler/plotly_resampler/aggregation/gap_handler_interface.py
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from typing import Optional, Tuple
  import numpy
label_desc: Use image node_id 2 for calling the AbstractGapHandler obj's underlying member method code with example usage: obj._get_gap_mask(x_agg) without return types