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 load(input_path):
      with open(os.path.join(input_path, "config.json")) as fIn:
          config = json.load(fIn)
  
      config["activation_function"] = import_from_string(
          config["activation_function"]
      )()
      model = Dense(**config)
      model.load_state_dict(
          torch.load(
              os.path.join(input_path, "pytorch_model.bin"),
              map_location=torch.device("cpu"),
          )
      )
      return model
  
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 7 for calling the Dense obj's underlying member method code with example usage: obj.load(input_path) and returns: model