code: def _create_block(
      self, in_channels, out_channels, with_fsdp, freeze_after_wrap_fsdp
  ):
      block = nn.Sequential(
          nn.Conv2d(in_channels, out_channels, kernel_size=3),
          nn.ReLU(inplace=True),
      )
      return block
  
imports: from enum import Enum
  from itertools import product
  import tempfile
  import pytest
  import torch
  import torch.multiprocessing
  import torch.nn
  from torch.nn.parallel import DistributedDataParallel
  import torch.optim
  from fairscale.fair_dev.testing.testing import dist_init, objects_are_equal, rmf, skip_if_single_gpu, teardown
  from fairscale.nn.data_parallel import FullyShardedDataParallel
label_desc: Use image node_id 4 for calling the NestedTrunkModel obj's underlying member method code with example usage: obj._create_block(in_channels, out_channels, with_fsdp, freeze_after_wrap_fsdp) and returns: block