code: def _dense_1b() -> NllbConfig:
      return NllbConfig(
          model_dim=1024,
          max_seq_len=1024,
          vocab_info=VocabularyInfo(
              size=256206, unk_idx=1, bos_idx=2, eos_idx=3, pad_idx=0
          ),
          num_encoder_layers=24,
          num_decoder_layers=24,
          num_encoder_attn_heads=16,
          num_decoder_attn_heads=16,
          ffn_inner_dim=1024 * 8,
          dropout_p=0.1,
      )
  
imports: from dataclasses import dataclass
  from functools import partial
  from typing import List, Optional, Tuple
  from torch.distributed.fsdp.wrap import transformer_auto_wrap_policy
  from fairseq2.data import VocabularyInfo
  from fairseq2.gang import Gang
  from fairseq2.models.transformer import TransformerEmbeddingFrontend, TransformerFrontend, TransformerModel
  from fairseq2.models.utils import ArchitectureRegistry
  from fairseq2.nn.embedding import Embedding, StandardEmbedding, init_scaled_embedding
  from fairseq2.nn.fsdp import FSDPWrapPolicy
  from fairseq2.nn.position_encoder import SinusoidalPositionEncoder
  from fairseq2.nn.projection import TiedProjection
  from fairseq2.nn.transformer import FeedForwardNetwork, MultiheadAttention, StandardFeedForwardNetwork, StandardMultiheadAttention, StandardTransformerDecoder, StandardTransformerDecoderLayer, StandardTransformerEncoder, StandardTransformerEncoderLayer, TransformerDecoder, TransformerDecoderLayer, TransformerEncoder, TransformerEncoderLayer, TransformerNormOrder, create_default_sdpa
  from fairseq2.typing import DataType, Device
label_desc: Use image node_id 1 for calling a global function with example usage: _dense_1b() and returns: NllbConfig