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
  ----------
  num_dim : int
      The number of dimensions of the tensor that will be masked
docstr_tok: ["Parameters","--","--","--","--","--","num_dim",":","int","The","number","of","dimensions","of","the","tensor","that","will","be","masked"]
code: def __init__(self, num_dim):
      """
      Parameters
      ----------
      num_dim : int
          The number of dimensions of the tensor that will be masked
      """
      self.mask_index = [None for _ in range(num_dim)]
  
imports: import logging
  import torch
label_desc: Use image node_id 1 to create a new CoarseMask object  with example: obj = CoarseMask(num_dim)