class_docstr: Coarse grained mask for a given tensor, here tensor could be weights,
  input tensor, or output tensor
class_docstr_tok: ["Coarse","grained","mask","for","a","given","tensor",",","here","tensor","could","be","weights",",","input","tensor",",","or","output","tensor"]
docstr: Merge another CoarseMask
  
  Parameters
  ----------
  cmask : CoarseMask
      Another CoarseMask to merge
  
  Returns
  -------
  list
      The member variable ```mask_index```
docstr_tok: ["Merge","another","CoarseMask","Parameters","--","--","--","--","--","cmask",":","CoarseMask","Another","CoarseMask","to","merge","Returns","--","--","--","-","list","The","member","variable","``","`","mask_index","``","`"]
code: def merge(self, cmask):
      """
      Merge another CoarseMask
  
      Parameters
      ----------
      cmask : CoarseMask
          Another CoarseMask to merge
  
      Returns
      -------
      list
          The member variable ```mask_index```
      """
      assert isinstance(cmask, CoarseMask)
      assert len(self.mask_index) == len(
          cmask.mask_index
      ), "Only masks with the same number of dimensions can be merged"
      for i, index in enumerate(self.mask_index):
          if index is None:
              self.mask_index[i] = cmask.mask_index[i]
          elif cmask.mask_index[i] is not None:
              self.mask_index[i] = CoarseMask.merge_index(
                  self.mask_index[i], cmask.mask_index[i]
              )
      return self.mask_index
  
imports: import logging
  import torch
label_desc: Use image node_id 4 for calling the CoarseMask obj's underlying member method code with example usage: obj.merge(cmask) and returns: self