class_docstr: Conditional wavenet.
  
  Args:
      n_quantize (int): Number of quantization.
      n_aux (int): Number of aux feature dimension.
      n_resch (int): Number of filter channels for residual block.
      n_skipch (int): Number of filter channels for skip connection.
      dilation_depth (int): Number of dilation depth
          (e.g. if set 10, max dilation = 2^(10-1)).
      dilation_repeat (int): Number of dilation repeat.
      kernel_size (int): Filter size of dilated causal convolution.
      upsampling_factor (int): Upsampling factor.
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code: def _postprocess(self, x):
      output = F.relu(x)
      output = self.conv_post_1(output)
      output = F.relu(output)  # B x C x T
      output = self.conv_post_2(output).transpose(1, 2)  # B x T x C
      return output
  
imports: import logging
  import sys
  import time
  import numpy
  import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 5 for calling the WaveNet obj's underlying member method code with example usage: obj._postprocess(x) and returns: output