code: def conv3x3(in_channels, out_channels, **kwargs):
      # The convolutional layers (for feature extraction) use standard layers from
      # `torch.nn`, since they do not require adaptation.
      # See `examples/maml/model.py` for comparison.
      return nn.Sequential(
          nn.Conv2d(
              in_channels,
              out_channels,
              kernel_size=3,
              padding=1,
              **kwargs,
          ),
          nn.BatchNorm2d(
              out_channels, momentum=1.0, track_running_stats=False
          ),
          nn.ReLU(),
          nn.MaxPool2d(2),
      )
  
imports: import torch.nn
  from torchmeta.modules import MetaModule, MetaLinear
label_desc: Use image node_id 1 for calling a global function with example usage: conv3x3(in_channels, out_channels) and returns: nn