class_docstr: ### VGG style model for CIFAR-10 classification
  
  This derives from the [generic VGG style architecture](../experiments/cifar10.html).
class_docstr_tok: ["#","#","#","VGG","style","model","for","CIFAR-10","classification","This","derives","from","the","[","generic","VGG","style","architecture","]","(","..","\/experiments\/cifar10.html",")","."]
docstr: Create a convolution layer and the activations
docstr_tok: ["Create","a","convolution","layer","and","the","activations"]
code: def conv_block(self, in_channels, out_channels) -> nn.Module:
      """
      Create a convolution layer and the activations
      """
      return nn.Sequential(
          # Convolution layer
          nn.Conv2d(
              in_channels, out_channels, kernel_size=3, padding=1
          ),
          # Batch normalization
          BatchNorm(out_channels, track_running_stats=False),
          # ReLU activation
          nn.ReLU(inplace=True),
      )
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/distillation/small.py
imports: import torch.nn
  from labml import experiment, logger
  from labml.configs import option
  from labml_nn.experiments.cifar10 import CIFAR10Configs, CIFAR10VGGModel
  from labml_nn.normalization.batch_norm import BatchNorm
label_desc: Use image node_id 1 for calling the SmallModel obj's underlying member method code with example usage: obj.conv_block(in_channels, out_channels) and returns: nn