text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  class FeedForwardNetwork(nn.Module):
      def __init__(self, input_size, hidden_size, output_size, dropout_rate=0):
          super(FeedForwardNetwork, self).__init__()
          self.dropout_rate = dropout_rate
          self.linear1 = nn.Linear(input_size, hidden_size)
          self.linear2 = nn.Linear(hidden_size, output_size)
  
      def forward(self, x):
          x_proj = F.dropout(F.relu(self.linear1(x)), p=self.dropout_rate, training=self.training)
          x_proj = self.linear2(x_proj)
          return x_proj
  
  
  class PoolerStartLogits(nn.Module):
      def __init__(self, hidden_size, num_classes):
          super(PoolerStartLogits, self).__init__()
          self.dense = nn.Linear(hidden_size, num_classes)
  
      def forward(self, hidden_states, p_mask=None):
          x = self.dense(hidden_states)
          return x
  
  class PoolerEndLogits(nn.Module):
      def __init__(self, hidden_size, num_classes):
          super(PoolerEndLogits, self).__init__()
          self.dense_0 = nn.Linear(hidden_size, hidden_size)
          self.activation = nn.Tanh()
          self.LayerNorm = nn.LayerNorm(hidden_size)
          self.dense_1 = nn.Linear(hidden_size, num_classes)
  
      def forward(self, hidden_states, start_positions=None, p_mask=None):
          x = self.dense_0(torch.cat([hidden_states, start_positions], dim=-1))
          x = self.activation(x)
          x = self.LayerNorm(x)
          x = self.dense_1(x)
          return x
  