code: def build_model(hparams: Dict) -> nn.Module:
      return nn.Sequential(
          nn.Conv2d(1, hparams["n_filters1"], 3, 1),
          nn.ReLU(),
          nn.Conv2d(
              hparams["n_filters1"],
              hparams["n_filters2"],
              3,
          ),
          nn.ReLU(),
          nn.MaxPool2d(2),
          nn.Dropout2d(hparams["dropout1"]),
          Flatten(),
          nn.Linear(144 * hparams["n_filters2"], 128),
          nn.ReLU(),
          nn.Dropout2d(hparams["dropout2"]),
          nn.Linear(128, 10),
          nn.LogSoftmax(),
      )
  
file_path: determined/e2e_tests/tests/fixtures/mnist_pytorch/model.py
imports: from typing import Any, Dict
  import torch
  from torch import nn
  from determined import pytorch
label_desc: Use image node_id 1 for calling a global function with example usage: build_model(hparams) and returns: nn