class_docstr: ### 🚧 Linear pooling for down-sampling
  
  This concatenates the consecutive tokens embeddings that need to be merged and do a linear
  transformation to map it to the size of a single token embedding.
code: def __init__(self):
      super().__init__()
      raise NotImplementedError
  
imports: from typing import List
  import torch
  from torch import nn
  from labml_helpers.module import Module
  from labml_nn.transformers import MultiHeadAttention, TransformerLayer
  from labml_nn.transformers.feed_forward import FeedForward
  from labml_nn.transformers.utils import subsequent_mask
label_desc: Use image node_id 1 to create a new LinearPoolingShortening object from inherited base classes: Module with example: obj = LinearPoolingShortening()