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 Transformer feed-forward network.
docstr_tok: ["Build","a","Transformer","feed-forward","network","."]
code: def build_ffn(self) -> FeedForwardNetwork:
      """Build a Transformer feed-forward network."""
      return GLUFeedForwardNetwork(
          self.config.model_dim,
          self.config.ffn_inner_dim,
          bias=False,
          inner_dim_scale=1.0,
          device=self.device,
          dtype=self.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 7 for calling the MistralBuilder obj's underlying member method code with example usage: obj.build_ffn() and returns: GLUFeedForwardNetwork