class_docstr: UnivNet Generator
  
  Originally from https://github.com/mindslab-ai/univnet/blob/master/model/generator.py.
class_docstr_tok: ["UnivNet","Generator","Originally","from","https",":","\/\/github.com\/mindslab-ai\/univnet\/blob\/master\/model\/generator.py","."]
docstr: Args:
      c (Tensor): the conditioning sequence of mel-spectrogram (batch, mel_channels, in_length)
      z (Tensor): the noise sequence (batch, noise_dim, in_length)
docstr_tok: ["Args",":","c","(","Tensor",")",":","the","conditioning","sequence","of","mel-spectrogram","(","batch",",","mel_channels",",","in_length",")","z","(","Tensor",")",":","the","noise","sequence","(","batch",",","noise_dim",",","in_length",")"]
code: def forward(self, c, z):
      """
      Args:
          c (Tensor): the conditioning sequence of mel-spectrogram (batch, mel_channels, in_length)
          z (Tensor): the noise sequence (batch, noise_dim, in_length)
  
      """
      z = self.conv_pre(z)  # (B, c_g, L)
  
      for res_block in self.res_stack:
          res_block.to(z.device)
          z = res_block(z, c)  # (B, c_g, L * s_0 * ... * s_i)
  
      z = self.conv_post(z)  # (B, 1, L * 256)
  
      return z
  
imports: from dataclasses import dataclass
  from enum import Enum
  from typing import Callable, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.nn.utils.parametrize
label_desc: Use image node_id 2 for calling the UnivNetGenerator obj's underlying member method code with example usage: obj.forward(c, z) and returns: z