class_docstr: Extraction of a single output embedding.
class_docstr_tok: ["Extraction","of","a","single","output","embedding","."]
code: def forward(self, hidden_states):
      # We "pool" the model by simply taking the hidden state corresponding
      # to the first token.
      first_token_tensor = hidden_states[:, 0]
      pooled_output = self.dense(first_token_tensor)
      pooled_output = self.activation(pooled_output)
      return pooled_output
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import logging
  import math
  import torch
  from torch import nn
  from towhee.models.layers.activations import swish, gelu
label_desc: Use image node_id 2 for calling the BertPooler obj's underlying member method code with example usage: obj.forward(hidden_states) and returns: pooled_output