code: def gpu_mem_usage():
      """
      Compute the GPU memory usage for the current device (GB).
      """
      if torch.cuda.is_available():
          mem_usage_bytes = torch.cuda.max_memory_allocated()
      else:
          mem_usage_bytes = 0
      return mem_usage_bytes / 1024**3
  
imports: import json
  import logging
  import math
  import numpy
  import os
  from datetime import datetime
  import psutil
  import torch
  import torchvision.io
  from fvcore.nn.activation_count import activation_count
  from fvcore.nn.flop_count import flop_count
  from matplotlib import pyplot
  from torch import nn
  from torchvision.utils import make_grid
  import slowfast.utils.logging
  import slowfast.utils.multiprocessing
  from slowfast.datasets.utils import pack_pathway_output
  from slowfast.models.batchnorm_helper import SubBatchNorm3d
  from slowfast.utils.env import pathmgr
label_desc: Use image node_id 3 for calling a global function with example usage: gpu_mem_usage() and returns: unknown