code: def forward(self, *, mel_spec):
      mel_spec_out = mel_spec
      for i in range(len(self.convolutions) - 1):
          mel_spec_out = F.dropout(
              torch.tanh(self.convolutions[i](mel_spec_out)),
              self.p_dropout,
              self.training,
          )
      mel_spec_out = F.dropout(
          self.convolutions[-1](mel_spec_out),
          self.p_dropout,
          self.training,
      )
  
      return mel_spec + mel_spec_out
  
imports: import torch
  from torch.autograd import Variable
  from torch.nn import functional
  from nemo.collections.tts.modules.submodules import Attention, ConvNorm, LinearNorm, Prenet
  from nemo.collections.tts.parts.utils.helpers import get_mask_from_lengths
  from nemo.core.classes import NeuralModule, typecheck
  from nemo.core.neural_types.elements import EmbeddedTextType, LengthsType, LogitsType, MelSpectrogramType, SequenceToSequenceAlignmentType
  from nemo.core.neural_types.neural_type import NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 4 for calling the Postnet obj's underlying member method code with example usage: obj.forward() and returns: unknown