code: def main():
      # Device
      device = torch.device("cuda:0")
  
      # Load layers in float16 into CPU. We convert the layers to int8 later, because doing that
      # on the fly after loading layers to GPU causes CUDA memory fragmentation
      # (about 3GB memory can get lost due to fragmentation).
      layer_generator = LayerGenerator(
          is_clone_layers=True,
          dtype=torch.float16,
          device=torch.device("cpu"),
      )
      # Load layers
      layers = list(layer_generator.load())
  
      # This reduces CUDA memory fragmentation
      for layer in monit.iterate(
          "Convert to int8", layers, is_children_silent=True
      ):
          layer_generator.post_load_prepare(
              layer,
              device=device,
              is_llm_int8=True,
              llm_int8_threshold=6.0,
          )
          layer.to(device)
  
      # Create `nn.Sequential` model
      model = nn.Sequential(*layers)
  
      # Run [evaluation harness](index.html)
      print(run_eval_harness(model, "half_precision", [], device))
  
imports: import torch
  from torch import nn
  from labml import monit
  from labml_nn.neox.evaluation import run_eval_harness
  from labml_nn.neox.model import LayerGenerator
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types