code: def Embedding(num_embeddings, embedding_dim, padding_idx):
      m = nn.Embedding(
          num_embeddings, embedding_dim, padding_idx=padding_idx
      )
      # nn.init.uniform_(m.weight, -0.1, 0.1)
      # nn.init.constant_(m.weight[padding_idx], 0)
      return m
  
imports: import argparse
  import math
  from collections.abc import Iterable
  import torch
  import torch.nn
  from examples.speech_recognition.data.data_utils import lengths_to_encoder_padding_mask
  from fairseq import utils
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, FairseqEncoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture
  from fairseq.modules import LinearizedConvolution, TransformerDecoderLayer, TransformerEncoderLayer, VGGBlock
label_desc: Use image node_id 3 for calling a global function with example usage: Embedding(num_embeddings, embedding_dim, padding_idx) and returns: m