class_docstr: This class implements an NMF encoder with a convolutional network
  
  Arguments
  ---------
  n_freq : int
      The number of frequency bins in the NMF dictionary
  n_comp : int
      Number of NMF components
  
  Example:
  --------
  >>> nmfencoder = NMFEncoder(513, 100)
  >>> X = torch.rand(1, 513, 240)
  >>> Hhat = nmfencoder(X)
  >>> print(Hhat.shape)
  torch.Size([1, 100, 240])
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docstr: Arguments:
  ---------
  X : torch.Tensor
      The input spectrogram Tensor with shape B x n_freq x T
  
  where B = Batchsize
        n_freq = nfft for the input spectrogram
        T = number of timepoints
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code: def forward(self, X):
      """
      Arguments:
      ---------
      X : torch.Tensor
          The input spectrogram Tensor with shape B x n_freq x T
  
      where B = Batchsize
            n_freq = nfft for the input spectrogram
            T = number of timepoints
      """
      return self.convenc(X)
  
imports: import torch.nn
  import torch.nn.functional
  import torch
  from speechbrain.lobes.models.PIQ import ResBlockAudio
label_desc: Use image node_id 2 for calling the NMFEncoder obj's underlying member method code with example usage: obj.forward(X) and returns: self