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.
class_docstr_tok: ["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","."]
code: def _generate_residual_forward(
      self,
      x,
      h,
      dil_sigmoid,
      dil_tanh,
      aux_1x1_sigmoid,
      aux_1x1_tanh,
      skip_1x1,
      res_1x1,
  ):
      output_sigmoid = dil_sigmoid(x)[:, :, -1:]
      output_tanh = dil_tanh(x)[:, :, -1:]
      aux_output_sigmoid = aux_1x1_sigmoid(h)
      aux_output_tanh = aux_1x1_tanh(h)
      output = torch.sigmoid(
          output_sigmoid + aux_output_sigmoid
      ) * torch.tanh(output_tanh + aux_output_tanh)
      skip = skip_1x1(output)
      output = res_1x1(output)
      output = output + x[:, :, -1:]  # B x C x 1
      return output, skip
  
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 7 for calling the WaveNet obj's underlying member method code with example usage: obj._generate_residual_forward(x, h, dil_sigmoid, dil_tanh, aux_1x1_sigmoid, aux_1x1_tanh, skip_1x1, res_1x1) and returns: output,  skip