code: def __init__(self, hparams):
      # NEW: Read hyperparameters provided for this trial.
      super(Net, self).__init__()
      self.conv1 = nn.Conv2d(1, hparams["n_filters1"], 3, 1)
      self.conv2 = nn.Conv2d(
          hparams["n_filters1"], hparams["n_filters2"], 3, 1
      )
      self.dropout1 = nn.Dropout(hparams["dropout1"])
      self.dropout2 = nn.Dropout(hparams["dropout2"])
      self.fc1 = nn.Linear(144 * hparams["n_filters2"], 128)
      self.fc2 = nn.Linear(128, 10)
  
file_path: determined/examples/tutorials/core_api_pytorch_mnist/model_def_adaptive.py
imports: from __future__ import print_function
  import argparse
  import pathlib
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torch.optim.lr_scheduler import StepLR
  from torchvision import datasets, transforms
  import determined
label_desc: Use image node_id 1 to create a new Net object from inherited base classes: nn with example: obj = Net(hparams)