code: def apply_compression_results(
      model, masks=None, masks_file=None, map_location=None
  ):
      """
      Apply the masks from ```masks_file``` to the model
      Note: this API is for inference, because it simply multiplies weights with
      corresponding masks when this API is called.
  
      Parameters
      ----------
      model : torch.nn.Module
          The model to be compressed
      masks : dict
          The pre-loaded dictionary of weight pruning masks
      masks_file : str
          The path of user provided mask file
      map_location : str
          the device on which masks are placed, same to map_location in ```torch.load```
      """
      if masks is not None:
          masks = masks
      elif masks_file is not None:
          masks = torch.load(masks_file, map_location)
      else:
          raise ValueError(
              "Either masks or masks_file must be passed to apply_compression_results"
          )
      for name, module in model.named_modules():
          if name in masks:
              module.weight.data = module.weight.data.mul_(
                  masks[name]["weight"]
              )
              if (
                  hasattr(module, "bias")
                  and module.bias is not None
                  and "bias" in masks[name]
              ):
                  module.bias.data = module.bias.data.mul_(
                      masks[name]["bias"]
                  )
  
label_desc: Use image node_id 1 for calling a global function with example usage: apply_compression_results(model, masks, masks_file, map_location) without return types