text: import torch
  import torch.nn as nn
  
  
  class RobertaClassificationHead(nn.Module):
      """Head for sentence-level classification tasks."""
  
      def __init__(self, config, value_head):
          super().__init__()
          self.value_head = value_head
          self.dense = nn.Linear(config.hidden_size, config.hidden_size)
          if value_head != 0:
              self.denseFeature1 = nn.Linear(value_head, config.hidden_size)
          self.dropout = nn.Dropout(config.hidden_dropout_prob)
          self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
  
      def forward(self, features, **kwargs):
          x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
          x = self.dropout(x)
          x = self.dense(x)
          if self.value_head != 0:
              x1 = kwargs.pop("externalFeatures", None)
              x1 = self.denseFeature1(x1)
              x = torch.mul(x, x1)
          x = torch.tanh(x)
          x = self.dropout(x)
          x = self.out_proj(x)
          return x
  