class_docstr: Multiscale Dilated Convolution from https://arxiv.org/pdf/1609.07093.pdf
class_docstr_tok: ["Multiscale","Dilated","Convolution","from","https",":","\/\/arxiv.org\/pdf\/1609.07093.pdf"]
code: def forward(self, x):
      _out = None
      for _l in self.d_convs:
          _x = torch.unsqueeze(_l(x), -1)
          _x = F.leaky_relu(_x, 0.2)
          if _out is None:
              _out = _x
          else:
              _out = torch.cat([_out, _x], axis=-1)
      x = torch.sum(_out, dim=-1)
      x = self.post_conv(x)
      x = F.leaky_relu(x, 0.2)  # @@
  
      return x
  
imports: import logging
  from typing import Any, Dict, List, Optional
  import torch
  import torch.nn.functional
  from torch.nn import Conv1d
  from torch.nn.utils import spectral_norm, weight_norm
  from espnet2.gan_svs.visinger2.visinger2_vocoder import MultiFrequencyDiscriminator
  from espnet2.gan_tts.hifigan.residual_block import ResidualBlock
  from espnet2.gan_tts.melgan.pqmf import PQMF
label_desc: Use image node_id 2 for calling the MDC obj's underlying member method code with example usage: obj.forward(x) and returns: x