code: def build_model():
      model = nn.Sequential(
          nn.Conv2d(NUM_CHANNELS, IMAGE_SIZE, kernel_size=(3, 3)),
          nn.ReLU(),
          nn.Conv2d(32, 32, kernel_size=(3, 3)),
          nn.ReLU(),
          nn.MaxPool2d((2, 2)),
          nn.Dropout2d(0.25),
          nn.Conv2d(32, 64, (3, 3), padding=1),
          nn.ReLU(),
          nn.Conv2d(64, 64, (3, 3)),
          nn.ReLU(),
          nn.MaxPool2d((2, 2)),
          nn.Dropout2d(0.25),
          Flatten(),
          nn.Linear(2304, 512),
          nn.ReLU(),
          nn.Dropout2d(0.5),
          nn.Linear(512, NUM_CLASSES),
      )
      return model
  
file_path: determined/examples/features/torch_batch_process_core_api_comparison/model.py
imports: import os
  from typing import Any, Dict, Optional, Sequence, Union
  import torch
  import torch.nn
  from determined.experimental import client
label_desc: Use image node_id 1 for calling a global function with example usage: build_model() and returns: model