code: def conv3x3(in_planes, out_planes, stride=1):
      """3x3 convolution with padding"""
      return torch.nn.Sequential(
          nn.Conv2d(
              in_planes,
              out_planes,
              kernel_size=3,
              stride=stride,
              padding=1,
              bias=False,
          ),
          nn.BatchNorm2d(out_planes),
      )
  
imports: import logging
  import math
  from functools import partial
  from typing import List, Optional
  import torch
  import torch.nn
  from fairscale.nn import checkpoint_wrapper
  from vissl.config import AttrDict
  from vissl.models.model_helpers import DropPath, to_2tuple, trunc_normal_
  from vissl.models.trunks import register_model_trunk
  from vissl.models.trunks.vision_transformer import Mlp
  from vissl.utils.fsdp_utils import fsdp_wrapper
  from vissl.utils.misc import set_torch_seed
label_desc: Use image node_id 1 for calling a global function with example usage: conv3x3(in_planes, out_planes, stride) and returns: torch