docstr: Returns score as estimated by discriminator
  
  Arguments
  ---------
  deg_spec : torch.Tensor
      The spectral features of the degraded utterance
  ref_spec : torch.Tensor
      The spectral features of the reference utterance
docstr_tok: ["Returns","score","as","estimated","by","discriminator","Arguments","--","--","--","--","-","deg_spec",":","torch.Tensor","The","spectral","features","of","the","degraded","utterance","ref_spec",":","torch.Tensor","The","spectral","features","of","the","reference","utterance"]
code: def est_score(self, deg_spec, ref_spec):
      """Returns score as estimated by discriminator
  
      Arguments
      ---------
      deg_spec : torch.Tensor
          The spectral features of the degraded utterance
      ref_spec : torch.Tensor
          The spectral features of the reference utterance
      """
      combined_spec = torch.cat(
          [deg_spec.unsqueeze(1), ref_spec.unsqueeze(1)], 1
      )
      return self.modules.discriminator(combined_spec)
  
imports: import os
  import sys
  import shutil
  import pickle
  import torch
  import torchaudio
  import speechbrain
  from pesq import pesq
  from enum import Enum, auto
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.utils.metric_stats import MetricStats
  from speechbrain.processing.features import spectral_magnitude
  from speechbrain.nnet.loss.stoi_loss import stoi_loss
  from speechbrain.utils.distributed import run_on_main
  from speechbrain.dataio.sampler import ReproducibleWeightedRandomSampler
label_desc: Use image node_id 8 for calling the MetricGanBrain obj's underlying member method code with example usage: obj.est_score(deg_spec, ref_spec) and returns: self