class_docstr: Performs pooling (max or mean) on the token embeddings.
  
  Using pooling, it generates from a variable sized sentence a fixed sized sentence embedding. This layer also allows to use the CLS token if it is returned by the underlying word embedding model.
  You can concatenate multiple poolings together.
  
  :param word_embedding_dimension: Dimensions for the word embeddings
  :param pooling_mode: Can be a string: mean/max/cls. If set, overwrites the other pooling_mode_* settings
  :param pooling_mode_cls_token: Use the first token (CLS token) as text representations
  :param pooling_mode_max_tokens: Use max in each dimension over all tokens.
  :param pooling_mode_mean_tokens: Perform mean-pooling
  :param pooling_mode_mean_sqrt_len_tokens: Perform mean-pooling, but divide by sqrt(input_length).
  :param pooling_mode_weightedmean_tokens: Perform (position) weighted mean pooling, see https://arxiv.org/abs/2202.08904
  :param pooling_mode_lasttoken: Perform last token pooling, see https://arxiv.org/abs/2202.08904 & https://arxiv.org/abs/2201.10005
code: def load(input_path):
      with open(os.path.join(input_path, "config.json")) as fIn:
          config = json.load(fIn)
  
      return Pooling(**config)
  
file_path: sentence-transformers/sentence_transformers/models/Pooling.py
imports: import torch
  from torch import Tensor
  from torch import nn
  from typing import Dict
  import os
  import json
label_desc: Use image node_id 8 for calling the Pooling obj's underlying member method code with example usage: obj.load(input_path) and returns: Pooling