code: def PositionalEmbedding(num_embeddings, embedding_dim, padding_idx):
      m = LearnedPositionalEmbedding(
          num_embeddings, embedding_dim, padding_idx
      )
      nn.init.normal_(m.weight, 0, 0.1)
      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 utils
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture
  from fairseq.modules import AdaptiveSoftmax, BeamableMM, FairseqDropout, GradMultiply, LearnedPositionalEmbedding, LinearizedConvolution
label_desc: Use image node_id 3 for calling a global function with example usage: PositionalEmbedding(num_embeddings, embedding_dim, padding_idx) and returns: m