code: def convert_dense_shape(mask):
      """
      Get the dense shape of the tensor after removing the sparsity
      values.
  
      Parameters
      ----------
      mask: torch.Tensor
          The mask tensor.
  
      Returns
      -------
      dense_shape: tuple
          The dense shape after removing the sparsity values.
      """
      assert isinstance(mask, torch.Tensor)
      n_dim = len(mask.size())
      dense_shape = []
      for dim in range(n_dim):
          _, remained = convert_to_coarse_mask(mask, dim)
          dense_shape.append(remained.size(0))
      return tuple(dense_shape)
  
file_path: nni/nni/compression/speedup/replacement.py
imports: import logging
  import math
  import torch
  import torch.nn
  from .error_code import EmptyLayerError, ShapeMisMatchError, InputsNumberError, OutputTypeError, UnBalancedGroupError
label_desc: Use image node_id 2 for calling a global function with example usage: convert_dense_shape(mask) and returns: tuple