code: def __init__(self, cfg: Wav2vec_UConfig, target_dict):
      super().__init__()
  
      self.cfg = cfg
      self.zero_index = (
          target_dict.index("<SIL>") if "<SIL>" in target_dict else 0
      )
      self.smoothness_weight = cfg.smoothness_weight
  
      output_size = len(target_dict)
      self.pad = target_dict.pad()
      self.eos = target_dict.eos()
      self.smoothing = cfg.smoothing
      self.smoothing_one_sided = cfg.smoothing_one_sided
      self.no_softmax = cfg.no_softmax
      self.gumbel = cfg.gumbel
      self.hard_gumbel = cfg.hard_gumbel
      self.last_acc = None
  
      self.gradient_penalty = cfg.gradient_penalty
      self.code_penalty = cfg.code_penalty
      self.mmi_weight = cfg.mmi_weight
      self.blank_weight = cfg.blank_weight
      self.blank_mode = cfg.blank_mode
      self.blank_index = (
          target_dict.index("<SIL>") if cfg.blank_is_sil else 0
      )
      assert self.blank_index != target_dict.unk()
  
      self.discriminator = Discriminator(output_size, cfg)
      for p in self.discriminator.parameters():
          p.param_group = "discriminator"
  
      self.pca_A = self.pca_b = None
      d = cfg.input_dim
  
      self.segmenter = SEGMENT_FACTORY[cfg.segmentation.type](
          cfg.segmentation
      )
  
      self.generator = Generator(d, output_size, cfg)
  
      for p in self.generator.parameters():
          p.param_group = "generator"
  
      for p in self.segmenter.parameters():
          p.param_group = "generator"
  
      self.max_temp, self.min_temp, self.temp_decay = cfg.temp
      self.curr_temp = self.max_temp
      self.update_num = 0
  
      if self.mmi_weight > 0:
          self.target_downsample_rate = cfg.target_downsample_rate
          self.decoder = nn.Linear(d, cfg.target_dim)
          for p in self.decoder.parameters():
              p.param_group = "generator"
  
imports: from dataclasses import dataclass
  from enum import Enum, auto
  import math
  import numpy
  from typing import Tuple, List, Optional, Dict
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import autograd
  from fairseq import checkpoint_utils, utils
  from fairseq.dataclass import FairseqDataclass
  from fairseq.models import BaseFairseqModel, register_model
  from fairseq.modules import SamePad, TransposeLast
label_desc: Use image node_id 5 to create a new Wav2vec_U object from inherited base classes: BaseFairseqModel with example: obj = Wav2vec_U(cfg, target_dict)