code: def _base() -> Wav2Vec2Config:
      encoder_config = _encoder_base()
  
      return Wav2Vec2Config(
          encoder_config,
          final_dim=256,
          final_proj_bias=True,
          temporal_mask_span_len=10,
          max_temporal_mask_prob=0.65,
          spatial_mask_span_len=10,
          max_spatial_mask_prob=0.0,
          quantized_dim=256,
          num_codebooks=2,
          num_codebook_entries=320,
          codebook_sampling_temperature=(2, 0.5, 0.999995),
          num_distractors=100,
          logit_temp=0.1,
          diversity_loss_weight=0.1,
      )
  
imports: from dataclasses import dataclass
  from typing import List, Optional, Tuple
  from torch.nn import GELU, SiLU
  from fairseq2.models.conformer import ConformerBlock, ConformerConvolution
  from fairseq2.models.feature_extractor import SequenceFeatureExtractor
  from fairseq2.models.utils import ArchitectureRegistry
  from fairseq2.models.wav2vec2.feature_extractor import Wav2Vec2FbankFeatureExtractor, Wav2Vec2FeatureExtractor
  from fairseq2.models.wav2vec2.frontend import Wav2Vec2Frontend
  from fairseq2.models.wav2vec2.masker import Wav2Vec2Masker
  from fairseq2.models.wav2vec2.model import Wav2Vec2Model
  from fairseq2.models.wav2vec2.position_encoder import Wav2Vec2PositionEncoder, Wav2Vec2StackedPositionEncoder
  from fairseq2.models.wav2vec2.vector_quantizer import GumbelVectorQuantizer, VectorQuantizer
  from fairseq2.nn.position_encoder import PositionEncoder, RotaryEncoder
  from fairseq2.nn.transformer import SDPA, FeedForwardNetwork, MultiheadAttention, RelativePositionalEncoding, RelativePositionSDPA, StandardFeedForwardNetwork, StandardMultiheadAttention, StandardTransformerEncoder, StandardTransformerEncoderLayer, 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: _base() and returns: Wav2Vec2Config