code: def sweep_n(niter, dtype):
      for ntensor in [4, 8, 16, 32, 64, 128, 256]:
          tensors = [
              torch.randn(256, random.randint(100, 200))
              for t in range(ntensor)
          ]
          nt_a = torch.nested.nested_tensor(
              tensors,
              dtype=dtype,
              device="cuda",
          )
          nt_b = torch.nested.nested_tensor(
              [t.t() for t in tensors],
              dtype=dtype,
              device="cuda",
          )
          runtime = bench(nt_a, nt_b, niter)
          nt_a_size = torch.ops.aten._nested_tensor_size(nt_a)
          lengths = nt_a_size[:, 1]
          print(
              ",".join(
                  map(
                      str,
                      [
                          ntensor,
                          dtype,
                          lengths.min().item(),
                          lengths.float().mean().item(),
                          lengths.max().item(),
                          runtime,
                      ],
                  )
              )
          )
  
imports: import argparse
  import random
  import torch
label_desc: Use image node_id 2 for calling a global function with example usage: sweep_n(niter, dtype) without return types