code: def __init__(self, n_inputs):
      super(MLP, self).__init__()
      # input to first hidden layer
      self.hidden1 = Linear(n_inputs, 10)
      kaiming_uniform_(self.hidden1.weight, nonlinearity="relu")
      self.act1 = ReLU()
      # second hidden layer
      self.hidden2 = Linear(10, 8)
      kaiming_uniform_(self.hidden2.weight, nonlinearity="relu")
      self.act2 = ReLU()
      # third hidden layer and output
      self.hidden3 = Linear(8, 1)
      xavier_uniform_(self.hidden3.weight)
      self.act3 = Sigmoid()
  
file_path: BigDL/python/orca/tutorial/xshards/ionosphere.py
imports: from torch.nn import Linear
  from torch.nn import ReLU
  from torch.nn import Sigmoid
  from torch.nn import Module
  from torch.optim import SGD
  from torch.nn import BCELoss
  from torch.nn.init import kaiming_uniform_
  from torch.nn.init import xavier_uniform_
  import bigdl.orca.data.pandas
  from bigdl.orca import init_orca_context, stop_orca_context
  from bigdl.orca.learn.pytorch import Estimator
  from bigdl.orca.learn.metrics import Accuracy
  from bigdl.orca.data.transformer import StringIndexer
label_desc: Use image node_id 1 to create a new MLP object from inherited base classes: Module with example: obj = MLP(n_inputs)