code: def divisible(channels: int, divisor: int) -> int:
      return int(np.ceil(channels / float(divisor))) * divisor
  
file_path: pytorch-toolbelt/pytorch_toolbelt/modules/heads/progressive_shuffle.py
imports: from typing import Optional, Callable, Mapping
  import numpy
  from torch import nn
  from pytorch_toolbelt.modules import instantiate_activation_block
  from pytorch_toolbelt.modules.interfaces import AbstractHead, FeatureMapsSpecification
  from pytorch_toolbelt.modules.normalization import NORM_BATCH, instantiate_normalization_block
label_desc: Use image node_id 1 for calling a global function with example usage: divisible(channels, divisor) and returns: unknown