code: def _resnet(c: Configs):
      """
      ### Create model
      """
      # [ResNet](index.html)
      base = ResNetBase(
          c.n_blocks,
          c.n_channels,
          c.bottlenecks,
          img_channels=3,
          first_kernel_size=c.first_kernel_size,
      )
      # Linear layer for classification
      classification = nn.Linear(c.n_channels[-1], 10)
  
      # Stack them
      model = nn.Sequential(base, classification)
      # Move the model to the device
      return model.to(c.device)
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/resnet/experiment.py
imports: from typing import List, Optional
  from torch import nn
  from labml import experiment
  from labml.configs import option
  from labml_nn.experiments.cifar10 import CIFAR10Configs
  from labml_nn.resnet import ResNetBase
label_desc: Use image node_id 1 for calling a global function with example usage: _resnet(c) and returns: model