class_docstr: Builds modules of an NLLB model as described in
  :cite:t:`https://doi.org/10.48550/arxiv.2207.04672`.
  
  To tweak the architecture, you can derive from this class and override the
  corresponding methods.
class_docstr_tok: ["Builds","modules","of","an","NLLB","model","as","described","in",":","cite",":","t",":","`","https",":","\/\/doi.org\/10.48550\/arxiv.2207.04672","`",".","To","tweak","the","architecture",",","you","can","derive","from","this","class","and","override","the","corresponding","methods","."]
docstr: :param config:
      The configuration.
  :param device:
      The device on which to initialize modules.
  :param dtype:
      The data type of module parameters and buffers.
docstr_tok: [":","param","config",":","The","configuration",".",":","param","device",":","The","device","on","which","to","initialize","modules",".",":","param","dtype",":","The","data","type","of","module","parameters","and","buffers","."]
code: def __init__(
      self,
      config: NllbConfig,
      *,
      device: Optional[Device] = None,
      dtype: Optional[DataType] = None,
  ) -> None:
      """
      :param config:
          The configuration.
      :param device:
          The device on which to initialize modules.
      :param dtype:
          The data type of module parameters and buffers.
      """
      self.config = config
  
      self.device, self.dtype = device, dtype
  
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 to create a new NllbBuilder object  with example: obj = NllbBuilder(config)