code: def make_linear_from_emb(emb):
      vocab_size, emb_size = emb.weight.shape
      lin_layer = nn.Linear(vocab_size, emb_size, bias=False)
      lin_layer.weight.data = emb.weight.data
      return lin_layer
  
file_path: lightseq/examples/training/fairseq/ls_finetune_bart/convert_lightseq_to_huggingface.py
imports: import argparse
  from pathlib import Path
  import fairseq
  import torch
  from packaging import version
  from torch import nn
  from transformers import BartConfig, BartForConditionalGeneration, BartForSequenceClassification, BartModel, BartTokenizer
  from lightseq.training.cli.fs_modules import LSBARTModel
  from fairseq.models.bart import BARTModel
label_desc: Use image node_id 2 for calling a global function with example usage: make_linear_from_emb(emb) and returns: lin_layer