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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docstr: Calculate forward propagation.
  
  Args:
      x (LongTensor): Quantized input waveform tensor with the shape  (B, T).
      h (Tensor): Auxiliary feature tensor with the shape  (B, n_aux, T).
  
  Returns:
      Tensor: Logits with the shape (B, T, n_quantize).
docstr_tok: ["Calculate","forward","propagation",".","Args",":","x","(","LongTensor",")",":","Quantized","input","waveform","tensor","with","the","shape","(","B",",","T",")",".","h","(","Tensor",")",":","Auxiliary","feature","tensor","with","the","shape","(","B",",","n_aux",",","T",")",".","Returns",":","Tensor",":","Logits","with","the","shape","(","B",",","T",",","n_quantize",")","."]
code: def forward(self, x, h):
      """Calculate forward propagation.
  
      Args:
          x (LongTensor): Quantized input waveform tensor with the shape  (B, T).
          h (Tensor): Auxiliary feature tensor with the shape  (B, n_aux, T).
  
      Returns:
          Tensor: Logits with the shape (B, T, n_quantize).
  
      """
      # preprocess
      output = self._preprocess(x)
      if self.upsampling_factor > 0:
          h = self.upsampling(h)
  
      # residual block
      skip_connections = []
      for i in range(len(self.dilations)):
          output, skip = self._residual_forward(
              output,
              h,
              self.dil_sigmoid[i],
              self.dil_tanh[i],
              self.aux_1x1_sigmoid[i],
              self.aux_1x1_tanh[i],
              self.skip_1x1[i],
              self.res_1x1[i],
          )
          skip_connections.append(skip)
  
      # skip-connection part
      output = sum(skip_connections)
      output = self._postprocess(output)
  
      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 2 for calling the WaveNet obj's underlying member method code with example usage: obj.forward(x, h) and returns: output