code: def _small() -> S2TTransformerConfig:
      return S2TTransformerConfig(
          model_dim=256,
          max_seq_len=1024,
          num_fbank_channels=80,
          target_vocab_info=VocabularyInfo(
              size=10000, unk_idx=3, bos_idx=0, eos_idx=2, pad_idx=1
          ),
          use_relative_pos=False,
          use_conformer=False,
          num_encoder_layers=12,
          num_decoder_layers=6,
          num_encoder_attn_heads=4,
          num_decoder_attn_heads=4,
          ffn_inner_dim=256 * 8,
          dropout_p=0.1,
          depthwise_conv_kernel_size=0,
      )
  
imports: from dataclasses import dataclass
  from typing import Optional
  from torch.nn import SiLU
  from fairseq2.data import VocabularyInfo
  from fairseq2.models.conformer import ConformerBlock, ConformerConvolution
  from fairseq2.models.s2t_transformer.feature_extractor import Conv1dFbankSubsampler
  from fairseq2.models.s2t_transformer.frontend import S2TTransformerFrontend
  from fairseq2.models.transformer import TransformerEmbeddingFrontend, TransformerFrontend, TransformerModel, init_final_projection
  from fairseq2.models.utils import ArchitectureRegistry
  from fairseq2.nn.embedding import StandardEmbedding, init_scaled_embedding
  from fairseq2.nn.position_encoder import PositionEncoder, SinusoidalPositionEncoder
  from fairseq2.nn.projection import Linear
  from fairseq2.nn.transformer import FeedForwardNetwork, MultiheadAttention, RelativePositionalEncoding, RelativePositionSDPA, 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 2 for calling a global function with example usage: _small() and returns: S2TTransformerConfig