code: def create_llama_model(
      config: LLaMAConfig,
      *,
      device: Optional[Device] = None,
      dtype: Optional[DataType] = None,
  ) -> TransformerDecoderModel:
      """Create a LLaMA model.
  
      :param config:
          The configuration.
      :param device:
          The device on which to initialize modules.
      :param dtype:
          The data type of module parameters and buffers.
      """
      return LLaMABuilder(
          config, device=device, dtype=dtype
      ).build_model()
  
imports: from dataclasses import dataclass
  from typing import Optional
  from fairseq2.data import VocabularyInfo
  from fairseq2.models.transformer import TransformerDecoderModel, TransformerEmbeddingFrontend, TransformerFrontend, init_final_projection
  from fairseq2.models.utils import ArchitectureRegistry
  from fairseq2.nn.embedding import StandardEmbedding
  from fairseq2.nn.lora import LoRAConfig
  from fairseq2.nn.normalization import LayerNorm, RMSNorm
  from fairseq2.nn.position_encoder import RotaryEncoder
  from fairseq2.nn.projection import Linear
  from fairseq2.nn.transformer import FeedForwardNetwork, GLUFeedForwardNetwork, MultiheadAttention, StandardMultiheadAttention, StandardTransformerDecoder, StandardTransformerDecoderLayer, TransformerDecoder, TransformerDecoderLayer, TransformerNormOrder, create_default_sdpa
  from fairseq2.typing import DataType, Device
label_desc: Use image node_id 8 for calling a global function with example usage: create_llama_model(config) and returns: LLaMABuilder