class_docstr: Builds modules of a Mistral model as described in
  :cite:t:`https://doi.org/10.48550/arXiv.2310.06825`.
  
  To tweak the architecture, you can derive from this class and override the
  corresponding methods.
docstr: Build a Layer Normalization module.
docstr_tok: ["Build","a","Layer","Normalization","module","."]
code: def build_layer_norm(
      self,
      model_dim: int,
      *,
      device: Optional[Device] = None,
      dtype: Optional[DataType] = None,
  ) -> LayerNorm:
      """Build a Layer Normalization module."""
      return RMSNorm(model_dim, bias=False, device=device, dtype=dtype)
  
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.normalization import LayerNorm, RMSNorm
  from fairseq2.nn.position_encoder import RotaryEncoder
  from fairseq2.nn.projection import Linear
  from fairseq2.nn.transformer import CausalAttentionMaskFactory, FeedForwardNetwork, GLUFeedForwardNetwork, LocalAttentionStateFactory, 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 the MistralBuilder obj's underlying member method code with example usage: obj.build_layer_norm(model_dim) and returns: RMSNorm