code: def PositionalEmbedding(num_embeddings, embedding_dim, padding_idx):
      m = LearnedPositionalEmbedding(
          num_embeddings, embedding_dim, padding_idx
      )
      m.weight.data.normal_(0, 0.1)
      return m
  
imports: import logging
  import math
  import os
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import checkpoint_utils
  from fairseq.incremental_decoding_utils import with_incremental_state
  from fairseq.models import CompositeEncoder, FairseqDecoder, FairseqEncoder, FairseqEncoderDecoderModel, register_model, register_model_architecture
  from fairseq.modules import DownsampledMultiHeadAttention, FairseqDropout, GradMultiply, LayerNorm, LearnedPositionalEmbedding, LinearizedConvolution
label_desc: Use image node_id 2 for calling a global function with example usage: PositionalEmbedding(num_embeddings, embedding_dim, padding_idx) and returns: m