code: def non_zero_channels(tensor):
      """Returns the indices of non-zero channels.
  
      Non-zero channels are channels that have at least one coefficient that
      is not zero.  Counting non-zero channels involves some tensor acrobatics.
      """
      if tensor.dim() != 4:
          raise ValueError("Expecting a 4D tensor")
  
      norms = distiller.norms.channels_lp_norm(tensor, p=1)
      nonzero_channels = torch.nonzero(norms)
      return nonzero_channels
  
imports: import argparse
  from collections import OrderedDict
  from copy import deepcopy
  import logging
  import operator
  import random
  import numpy
  import torch
  import torch.nn
  import torch.backends.cudnn
  import yaml
  import inspect
label_desc: Use image node_id 18 for calling a global function with example usage: non_zero_channels(tensor) and returns: nonzero_channels