code: def Embedding(num_embeddings, embedding_dim, padding_idx):
      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)
      return m
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import options, utils
  from fairseq.models import FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture, transformer
  from fairseq.modules import AdaptiveSoftmax, LearnedPositionalEmbedding, MultiheadAttention, SinusoidalPositionalEmbedding
  from pytorch_translate import transformer, vocab_reduction
  from pytorch_translate.average_attention import AverageAttention, AverageWindowAttention
  from pytorch_translate.utils import torch_find
label_desc: Use image node_id 1 for calling a global function with example usage: Embedding(num_embeddings, embedding_dim, padding_idx) and returns: m