code: def _count_convNd(self, m, x, y):
      cin = m.in_channels
      kernel_ops = torch.zeros(m.weight.size()[2:]).numel()
      output_size = torch.zeros(y.size()[2:]).numel()
      cout = y.size()[1]
  
      if hasattr(m, "weight_mask"):
          cout = m.weight_mask.sum() // (cin * kernel_ops)
  
      total_ops = (
          cout * output_size * kernel_ops * cin // m.groups
      )  # cout x oW x oH
  
      if self._count_bias:
          bias_flops = 1 if m.bias is not None else 0
          total_ops += cout * output_size * 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 4 for calling the ModelProfiler obj's underlying member method code with example usage: obj._count_convNd(m, x, y) and returns: self