code: def transformer_iwslt_de_en(args):
      args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
      args.encoder_ffn_embed_dim = getattr(
          args, "encoder_ffn_embed_dim", 1024
      )
      args.encoder_attention_heads = getattr(
          args, "encoder_attention_heads", 4
      )
      args.encoder_layers = getattr(args, "encoder_layers", 6)
      args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
      args.decoder_ffn_embed_dim = getattr(
          args, "decoder_ffn_embed_dim", 1024
      )
      args.decoder_attention_heads = getattr(
          args, "decoder_attention_heads", 4
      )
      args.decoder_layers = getattr(args, "decoder_layers", 6)
      base_architecture(args)
  
imports: import torch
  import torch.nn
  from fairseq import utils
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture
  from fairseq.models.fairseq_encoder import EncoderOut
  from fairseq.modules import LayerNorm
  from lightseq.training.ops.pytorch.layer_base import TransformerEmbeddingLayerBase
  from lightseq.training.ops.pytorch.quantization import QuantLinear, disable_quant, enable_quant, ptq_mode, qat_mode
label_desc: Use image node_id 3 for calling a global function with example usage: transformer_iwslt_de_en(args) without return types