code: def __init__(self, args, embed_tokens):
      super().__init__()
      self.dropout = args.dropout
      embed_dim = embed_tokens.embedding_dim
      self.padding_idx = embed_tokens.padding_idx
      self.embed_tokens = embed_tokens
      self.embed_scale = math.sqrt(embed_dim)
      self.embed_positions = fairseq_transformer.PositionalEmbedding(
          1024,
          embed_dim,
          self.padding_idx,
          learned=args.encoder_learned_pos,
      )
  
imports: import abc
  import math
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.models import FairseqIncrementalDecoder, transformer
  from pytorch_translate import rnn_cell, utils, vocab_reduction
  from pytorch_translate.research.lexical_choice import lexical_translation
label_desc: Use image node_id 1 to create a new TransformerEmbedding object from inherited base classes: nn with example: obj = TransformerEmbedding(args, embed_tokens)