text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  ## TODO: Complete this classifier
  class SimpleNet(nn.Module):
      
      ## TODO: Define the init function
      def __init__(self, input_dim, hidden_dim, output_dim):
          '''Defines layers of a neural network.
             :param input_dim: Number of input features
             :param hidden_dim: Size of hidden layer(s)
             :param output_dim: Number of outputs
           '''
          super(SimpleNet, self).__init__()
          
          # define all layers, here
          self.layer1 = nn.Sequential(
              nn.Linear(in_features=input_dim, out_features=hidden_dim),
              nn.Dropout(0.3),
              nn.ReLU()
          )
          self.layer2 = nn.Sequential(
              nn.Linear(in_features=hidden_dim, out_features=output_dim),
              nn.Sigmoid()
          )
      
      ## TODO: Define the feedforward behavior of the network
      def forward(self, x):
          '''Feedforward behavior of the net.
             :param x: A batch of input features
             :return: A single, sigmoid activated value
           '''
          # your code, here
          x = self.layer1(x)
          x = self.layer2(x)
          
          return x.squeeze()