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: Judge if the mask is a subset of another CoarseMask.
docstr_tok: ["Judge","if","the","mask","is","a","subset","of","another","CoarseMask","."]
code: def __lt__(self, other):
      """
      Judge if the mask is a subset of another CoarseMask.
      """
      assert isinstance(other, CoarseMask)
      for dim, _ in enumerate(self.mask_index):
          # if self has more dimensions
          if dim >= len(other.mask_index):
              return False
          if self.mask_index[dim] is None:
              # if no mask on this dimension, then we have less
              # masks then the other CoraseMask.
              continue
          elif other.mask_index[dim] is None:
              return False
          else:
              s1 = set(self.mask_index[dim].tolist())
              s2 = set(other.mask_index[dim].tolist())
              if not s1 < s2:
                  return False
      return True
  
imports: import logging
  import torch
label_desc: Use image node_id 8 for calling the CoarseMask obj's underlying member method code with example usage: obj.__lt__(other) and returns: True, False, False, False