class_docstr: Feed-forward function with  activiation function.
  
  This layer takes a fixed-sized sentence embedding and passes it through a feed-forward layer. Can be used to generate deep averaging networks (DAN).
  
  :param in_features: Size of the input dimension
  :param out_features: Output size
  :param bias: Add a bias vector
  :param activation_function: Pytorch activation function applied on output
  :param init_weight: Initial value for the matrix of the linear layer
  :param init_bias: Initial value for the bias of the linear layer
code: def get_sentence_embedding_dimension(self) -> int:
      return self.out_features
  
file_path: sentence-transformers/sentence_transformers/models/Dense.py
imports: import torch
  from torch import Tensor
  from torch import nn
  from typing import Dict
  import os
  import json
  from ..util import fullname, import_from_string
label_desc: Use image node_id 3 for calling the Dense obj's underlying member method code with example usage: obj.get_sentence_embedding_dimension() and returns: self