code: def linear_block(in_features, out_features, p_drop, *args, **kwargs):
      return nn.Sequential(
          nn.Linear(in_features, out_features),
          nn.ReLU(),
          nn.Dropout(p=p_drop),
      )
  
file_path: BigDL/python/orca/tutorial/xshards/tabular_playground_series.py
imports: from sklearn.model_selection import train_test_split
  import torch
  import torch.nn
  import torch.optim
  from bigdl.orca import init_orca_context, stop_orca_context
  import bigdl.orca.data.pandas
  from bigdl.orca.data.transformer import *
  from bigdl.orca.learn.pytorch import Estimator
  from bigdl.orca.learn.metrics import Accuracy
label_desc: Use image node_id 5 for calling a global function with example usage: linear_block(in_features, out_features, p_drop) and returns: nn