class_docstr: ### VGG model for CIFAR-10 classification
class_docstr_tok: ["#","#","#","VGG","model","for","CIFAR-10","classification"]
docstr: Convolution and activation combined
docstr_tok: ["Convolution","and","activation","combined"]
code: def conv_block(self, in_channels, out_channels) -> nn.Module:
      """
      Convolution and activation combined
      """
      return nn.Sequential(
          nn.Conv2d(
              in_channels, out_channels, kernel_size=3, padding=1
          ),
          nn.ReLU(inplace=True),
      )
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/experiments/cifar10.py
imports: from typing import List
  import torch.nn
  from labml import lab
  from labml.configs import option
  from labml_helpers.datasets.cifar10 import CIFAR10Configs
  from labml_helpers.module import Module
  from labml_nn.experiments.mnist import MNISTConfigs
label_desc: Use image node_id 1 for calling the CIFAR10VGGModel obj's underlying member method code with example usage: obj.conv_block(in_channels, out_channels) and returns: nn