docstr: Args:
      x (Tensor): conditioning input tensor.
  
  Returns:
      Tensor: output waveform.
  
  Shapes:
      x: [B, C, T]
      Tensor: [B, 1, T]
docstr_tok: ["Args",":","x","(","Tensor",")",":","conditioning","input","tensor",".","Returns",":","Tensor",":","output","waveform",".","Shapes",":","x",":","[","B",",","C",",","T","]","Tensor",":","[","B",",","1",",","T","]"]
code: def inference(self, c):
      """
      Args:
          x (Tensor): conditioning input tensor.
  
      Returns:
          Tensor: output waveform.
  
      Shapes:
          x: [B, C, T]
          Tensor: [B, 1, T]
      """
      c = c.to(self.conv_pre.weight.device)
      c = torch.nn.functional.pad(
          c,
          (self.inference_padding, self.inference_padding),
          "replicate",
      )
      return self.forward(c)
  
imports: import torch
  from torch import nn
  from torch.nn import Conv1d, ConvTranspose1d
  from torch.nn import functional
  from torch.nn.utils.parametrizations import weight_norm
  from torch.nn.utils.parametrize import remove_parametrizations
  from TTS.utils.io import load_fsspec
label_desc: Use image node_id 3 for calling the HifiganGenerator obj's underlying member method code with example usage: obj.inference(c) and returns: self