code: def projector_word_embeddings(
      learn=None,
      layer=None,
      vocab=None,
      limit=-1,
      start=0,
      log_dir=None,
  ):
      "Extracts and exports word embeddings from language models embedding layers"
      if not layer:
          if isinstance(learn, LMLearner):
              layer = learn.model[0].encoder
          elif isinstance(learn, TextLearner):
              layer = learn.model[0].module.encoder
      emb = layer.weight
      img = torch.full((len(emb), 3, 8, 8), 0.7)
      vocab = learn.dls.vocab[0] if vocab == None else vocab
      vocab = list(map(lambda x: f"{x}_", vocab))
      writer = SummaryWriter(log_dir=log_dir)
      end = start + limit if limit >= 0 else -1
      writer.add_embedding(
          emb[start:end],
          metadata=vocab[start:end],
          label_img=img[start:end],
      )
      writer.close()
  
imports: from __future__ import annotations
  from ..basics import *
  import tensorboard
  from torch.utils.tensorboard import SummaryWriter
  from .fp16 import ModelToHalf
  from .hook import hook_output
  from ..text.all import LMLearner, TextLearner
  from ..vision.data import *
  from ..vision.core import TensorPoint, TensorBBox
label_desc: Use image node_id 5 for calling a global function with example usage: projector_word_embeddings(learn, layer, vocab, limit, start, log_dir) without return types