class_docstr: Target Speaker Extraction Frontend model
class_docstr_tok: ["Target","Speaker","Extraction","Frontend","model"]
code: def enhance(self, feature_mix):
      if self.normalize:
          normfac = feature_mix.abs().max() * 1.1 + 1e-5
          feature_mix = feature_mix / normfac
  
      return self.diffusion.enhance(feature_mix)
  
imports: import contextlib
  from typing import Dict, List, Optional, OrderedDict, Tuple
  import torch
  from typeguard import check_argument_types
  from espnet2.enh.decoder.abs_decoder import AbsDecoder
  from espnet2.enh.diffusion.abs_diffusion import AbsDiffusion
  from espnet2.enh.encoder.abs_encoder import AbsEncoder
  from espnet2.enh.espnet_model import ESPnetEnhancementModel
  from espnet2.enh.extractor.abs_extractor import AbsExtractor
  from espnet2.enh.loss.criterions.tf_domain import FrequencyDomainLoss
  from espnet2.enh.loss.criterions.time_domain import TimeDomainLoss
  from espnet2.enh.loss.wrappers.abs_wrapper import AbsLossWrapper
  from espnet2.torch_utils.device_funcs import force_gatherable
  from espnet2.train.abs_espnet_model import AbsESPnetModel
label_desc: Use image node_id 3 for calling the ESPnetDiffusionModel obj's underlying member method code with example usage: obj.enhance(feature_mix) and returns: self