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 __init__(
      self,
      in_features: int,
      out_features: int,
      bias: bool = True,
      activation_function=nn.Tanh(),
      init_weight: Tensor = None,
      init_bias: Tensor = None,
  ):
      super(Dense, self).__init__()
      self.in_features = in_features
      self.out_features = out_features
      self.bias = bias
      self.activation_function = activation_function
      self.linear = nn.Linear(in_features, out_features, bias=bias)
  
      if init_weight is not None:
          self.linear.weight = nn.Parameter(init_weight)
  
      if init_bias is not None:
          self.linear.bias = nn.Parameter(init_bias)
  
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 1 to create a new Dense object from inherited base classes: nn with example: obj = Dense(in_features, out_features, bias, activation_function, init_weight, init_bias)