code: def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
      """3x3 convolution with padding"""
      return torch.nn.Conv2d(
          in_planes,
          out_planes,
          kernel_size=3,
          stride=stride,
          padding=dilation,
          groups=groups,
          bias=False,
          dilation=dilation,
      )
  
imports: import unittest
  import torch
  from torch.fx import symbolic_trace
  from torch.fx.experimental.unify_refinements import infer_symbolic_types
  from torch.fx.experimental.refinement_types import Equality
  from torch.fx.tensor_type import TensorType, Dyn, is_consistent, is_more_precise
  from torch.fx.annotate import annotate
  from torch.fx.experimental.graph_gradual_typechecker import GraphTypeChecker, broadcast_types, Refine
  from torch.fx.experimental.rewriter import RewritingTracer
  from torch.fx import GraphModule
  from torch.fx.passes.shape_prop import ShapeProp
  from torch.testing._internal.common_utils import TestCase
  import sympy
label_desc: Use image node_id 1 for calling a global function with example usage: conv3x3(in_planes, out_planes, stride, groups, dilation) and returns: torch