code: def Embedding(num_embeddings, embedding_dim, padding_idx=None):
      m = nn.Embedding(
          num_embeddings, embedding_dim, padding_idx=padding_idx
      )
      nn.init.normal_(m.weight, mean=0, std=embedding_dim**-0.5)
      return m
  
imports: import logging
  import torch
  from torch import nn
  from fairseq import utils
  from fairseq.data.data_utils import lengths_to_padding_mask
  from fairseq.models import FairseqEncoder, FairseqEncoderModel, register_model, register_model_architecture
  from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
  from fairseq.models.text_to_speech.tacotron2 import Postnet
  from fairseq.modules import FairseqDropout, LayerNorm, MultiheadAttention, PositionalEmbedding
label_desc: Use image node_id 2 for calling a global function with example usage: Embedding(num_embeddings, embedding_dim, padding_idx) and returns: m