docstr: Perform inference.
  Args:
      c (Tensor): Local conditioning auxiliary features :math:`(B, C, T)`.
  Returns:
      Tensor: Output tensor (T, out_channels)
docstr_tok: ["Perform","inference",".","Args",":","c","(","Tensor",")",":","Local","conditioning","auxiliary","features",":","math",":","`","(","B",",","C",",","T",")","`",".","Returns",":","Tensor",":","Output","tensor","(","T",",","out_channels",")"]
code: def inference(self, c):
      """Perform inference.
      Args:
          c (Tensor): Local conditioning auxiliary features :math:`(B, C, T)`.
      Returns:
          Tensor: Output tensor (T, out_channels)
      """
      x = torch.randn([c.shape[0], self.in_channels, c.shape[2]])
      x = x.to(self.first_conv.bias.device)
  
      c = c.to(next(self.parameters()))
      return self.forward(c)
  
file_path: TTS/TTS/vocoder/models/univnet_generator.py
imports: from typing import List
  import numpy
  import torch
  import torch.nn.functional
  from torch.nn.utils import parametrize
  from TTS.vocoder.layers.lvc_block import LVCBlock
label_desc: Use image node_id 7 for calling the UnivnetGenerator obj's underlying member method code with example usage: obj.inference(c) and returns: self