code: def TransformerTokenEmbedding(
      num_embeddings, embedding_dim, padding_idx, freeze_embed=False
  ):
      """
      Different weight initialization from common_layers.Embedding
      """
      m = nn.Embedding(
          num_embeddings, embedding_dim, padding_idx=padding_idx
      )
      nn.init.normal_(m.weight, mean=0, std=embedding_dim**-0.5)
      nn.init.constant_(m.weight[padding_idx], 0)
      if freeze_embed:
          m.weight.requires_grad = False
      return m
  
imports: import abc
  import math
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.models import FairseqIncrementalDecoder, transformer
  from pytorch_translate import rnn_cell, utils, vocab_reduction
  from pytorch_translate.research.lexical_choice import lexical_translation
label_desc: Use image node_id 4 for calling a global function with example usage: TransformerTokenEmbedding(num_embeddings, embedding_dim, padding_idx, freeze_embed) and returns: m