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: Parameters
  ----------
  index_a : tensor
      One index (1-dimension) tensor
  index_b : tensor
      The other index (1-dimension) tensor
  
  Returns
  -------
  tensor
      The merged index (1-dimension) tensor
      Note that: the output tensor will be moved
      to the same device as index_a.
docstr_tok: ["Parameters","--","--","--","--","--","index_a",":","tensor","One","index","(","1-dimension",")","tensor","index_b",":","tensor","The","other","index","(","1-dimension",")","tensor","Returns","--","--","--","-","tensor","The","merged","index","(","1-dimension",")","tensor","Note","that",":","the","output","tensor","will","be","moved","to","the","same","device","as","index_a","."]
code: def merge_index(index_a, index_b):
      """
      Parameters
      ----------
      index_a : tensor
          One index (1-dimension) tensor
      index_b : tensor
          The other index (1-dimension) tensor
  
      Returns
      -------
      tensor
          The merged index (1-dimension) tensor
          Note that: the output tensor will be moved
          to the same device as index_a.
      """
      device = index_a.device
      s = set()
      for num in index_a.tolist():
          # we need to transfer the tensor to list here
          # first, directly traversing the tensor by for
          # loop will return the list of tensor(x) object,
          # even the value are the same, but they are different
          # tensor objects, so the set will contains multiple
          # tensor objects that has the same value. For example
          # for num in torch.ones(2):
          #   s.add(num)
          # s will be {tensor(1), tensor(1)}
          s.add(num)
      for num in index_b.tolist():
          s.add(num)
      # move the output tensor to the same device with index_a
      return torch.tensor(sorted(s)).to(device)
  
imports: import logging
  import torch
label_desc: Use image node_id 3 for calling the CoarseMask obj's underlying member method code with example usage: obj.merge_index(index_a, index_b) and returns: torch