code: def _conv_block(in_channels: int, out_channels: int):
      return nn.Sequential(
          nn.Conv2d(in_channels, out_channels, kernel_size=(3, 3)),
          nn.BatchNorm2d(out_channels),
          nn.ReLU(),
      )
  
imports: import functools
  import os
  import tempfile
  from parameterized import parameterized
  import pytest
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  from torch.optim import Adam
  from fairscale.fair_dev.testing.testing import in_temporary_directory, skip_if_single_gpu, temp_files_ctx
  from fairscale.nn import FullyShardedDataParallel
  from tests.nn.data_parallel.test_fsdp import DistributedTest, MixtureOfExperts, rename_test, spawn_and_init
label_desc: Use image node_id 2 for calling the ConvolutionalModel obj's underlying member method code with example usage: obj._conv_block(in_channels, out_channels) and returns: nn