code: import torch
  import torch.nn as nn
  from torch.autograd import Variable
  import torch.nn.functional as F
  import torch.utils.data as Data
  import torch.optim as optim
  from torch.utils.data import Dataset, DataLoader, WeightedRandomSampler
  
  class MultipleRegression(nn.Module):
      def __init__(self, num_features):
          super(MultipleRegression, self).__init__()
          self.fc1 = nn.Linear(num_features, 64)
          self.fc2 = nn.Linear(64, 128)
  #         self.fc3 = nn.Linear(128, 64)
          self.output = nn.Linear(128, 1)
  
          self.act = nn.Sigmoid()
  
      def forward(self, inputs):
          x = self.act(self.fc1(inputs))
          x = self.act(self.fc2(x))
  #         x = self.act(self.fc3(x))
          x = self.output(x)
  
          return x
  
      def predict(self, test_inputs):
          x = self.act(self.fc1(test_inputs))
          x = self.act(self.fc2(x))
  #         x = self.act(self.fc3(x))
          x = self.output(x)
  
          return x
  
apis: torch.nn.Sigmoid torch.nn.Linear