code: def _count_linear(self, m, x, y):
      out_features = m.out_features
      if hasattr(m, "weight_mask"):
          out_features = m.weight_mask.sum() // m.in_features
      total_ops = out_features * m.in_features
  
      if self._count_bias:
          bias_flops = 1 if m.bias is not None else 0
          total_ops += out_features * bias_flops
  
      return self._get_result(m, total_ops)
  
imports: import functools
  from collections import Counter
  from prettytable import PrettyTable
  import torch
  import torch.nn
  from torch.nn.utils.rnn import PackedSequence
  from nni.compression.base.wrapper import ModuleWrapper
label_desc: Use image node_id 5 for calling the ModelProfiler obj's underlying member method code with example usage: obj._count_linear(m, x, y) and returns: self