code: def benchmark(self):
      x = [torch.ones(200, 200) for i in range(30)]
      with Timer() as big1:
          torch.save(
              x,
              "big_tensor.zip",
              _use_new_zipfile_serialization=use_new,
          )
  
      with Timer() as big2:
          v = torch.load("big_tensor.zip")
  
      x = [torch.ones(10, 10) for i in range(200)]
      with Timer() as small1:
          torch.save(
              x,
              "small_tensor.zip",
              _use_new_zipfile_serialization=use_new,
          )
  
      with Timer() as small2:
          v = torch.load("small_tensor.zip")
  
      return {
          "Big Tensors Save": big1.ms_duration,
          "Big Tensors Load": big2.ms_duration,
          "Small Tensors Save": small1.ms_duration,
          "Small Tensors Load": small2.ms_duration,
      }
  
imports: import torch
  from pyarkbench import Benchmark, default_args, Timer
label_desc: Use image node_id 1 for calling the Basic obj's underlying member method code with example usage: obj.benchmark() and returns: dict