code: def get_setting(ctx):
      from fate.ml.nn.dataset.table import TableDataset
  
      # prepare data
      if ctx.is_on_guest:
          ds = TableDataset(to_tensor=True)
          ds.load("../data/breast_hetero_guest.csv")
  
          bottom_model = t.nn.Sequential(
              t.nn.Linear(10, 8),
              t.nn.ReLU(),
          )
          top_model = t.nn.Sequential(t.nn.Linear(8, 1), t.nn.Sigmoid())
          model = HeteroNNModelGuest(
              top_model=top_model,
              bottom_model=bottom_model,
              agglayer_arg=SSHEArgument(
                  guest_in_features=8,
                  host_in_features=8,
                  out_features=8,
                  layer_lr=0.01,
              ),
          )
  
          optimizer = t.optim.Adam(model.parameters(), lr=0.01)
          loss = t.nn.BCELoss()
  
      else:
          ds = TableDataset(to_tensor=True)
          ds.load("../data/breast_hetero_host.csv")
          bottom_model = t.nn.Sequential(
              t.nn.Linear(20, 8),
              t.nn.ReLU(),
          )
  
          model = HeteroNNModelHost(
              bottom_model=bottom_model,
              agglayer_arg=SSHEArgument(
                  guest_in_features=8,
                  host_in_features=8,
                  out_features=8,
                  layer_lr=0.01,
              ),
          )
          optimizer = t.optim.Adam(model.parameters(), lr=0.01)
          loss = None
  
      args = TrainingArguments(
          num_train_epochs=3, per_device_train_batch_size=256
      )
  
      return ds, model, optimizer, loss, args
  
file_path: FATE/examples/launchers/sshe_nn_launcher.py
imports: import torch
  from fate.arch import Context
  from fate.ml.nn.hetero.hetero_nn import HeteroNNTrainerGuest, HeteroNNTrainerHost, TrainingArguments
  from fate.ml.nn.model_zoo.hetero_nn_model import HeteroNNModelGuest, HeteroNNModelHost
  from fate.ml.nn.model_zoo.hetero_nn_model import SSHEArgument
label_desc: Use image node_id 3 for calling a global function with example usage: get_setting(ctx) and returns: ds,  model,  optimizer,  loss,  args